File size: 78,558 Bytes
6fa4bc9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 | {
"paper_id": "P06-1004",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T09:23:54.845149Z"
},
"title": "Minimum Cut Model for Spoken Lecture Segmentation",
"authors": [
{
"first": "Igor",
"middle": [],
"last": "Malioutov",
"suffix": "",
"affiliation": {
"laboratory": "Artificial Intelligence Laboratory",
"institution": "Massachusetts Institute of Technology",
"location": {}
},
"email": "igorm@csail.mit.edu"
},
{
"first": "Regina",
"middle": [],
"last": "Barzilay",
"suffix": "",
"affiliation": {
"laboratory": "Artificial Intelligence Laboratory",
"institution": "Massachusetts Institute of Technology",
"location": {}
},
"email": "regina@csail.mit.edu"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "We consider the task of unsupervised lecture segmentation. We formalize segmentation as a graph-partitioning task that optimizes the normalized cut criterion. Our approach moves beyond localized comparisons and takes into account longrange cohesion dependencies. Our results demonstrate that global analysis improves the segmentation accuracy and is robust in the presence of speech recognition errors.",
"pdf_parse": {
"paper_id": "P06-1004",
"_pdf_hash": "",
"abstract": [
{
"text": "We consider the task of unsupervised lecture segmentation. We formalize segmentation as a graph-partitioning task that optimizes the normalized cut criterion. Our approach moves beyond localized comparisons and takes into account longrange cohesion dependencies. Our results demonstrate that global analysis improves the segmentation accuracy and is robust in the presence of speech recognition errors.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "The development of computational models of text structure is a central concern in natural language processing. Text segmentation is an important instance of such work. The task is to partition a text into a linear sequence of topically coherent segments and thereby induce a content structure of the text. The applications of the derived representation are broad, encompassing information retrieval, question-answering and summarization.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Not surprisingly, text segmentation has been extensively investigated over the last decade. Following the first unsupervised segmentation approach by Hearst (1994) , most algorithms assume that variations in lexical distribution indicate topic changes. When documents exhibit sharp variations in lexical distribution, these algorithms are likely to detect segment boundaries accurately. For example, most algorithms achieve high performance on synthetic collections, generated by concatenation of random text blocks (Choi, 2000) . The difficulty arises, however, when transitions between topics are smooth and distributional variations are subtle. This is evident in the performance of existing unsupervised algorithms on less structured datasets, such as spoken meeting transcripts (Galley et al., 2003) . Therefore, a more refined analysis of lexical distribution is needed.",
"cite_spans": [
{
"start": 150,
"end": 163,
"text": "Hearst (1994)",
"ref_id": "BIBREF8"
},
{
"start": 516,
"end": 528,
"text": "(Choi, 2000)",
"ref_id": "BIBREF2"
},
{
"start": 783,
"end": 804,
"text": "(Galley et al., 2003)",
"ref_id": "BIBREF4"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Our work addresses this challenge by casting text segmentation in a graph-theoretic framework. We abstract a text into a weighted undirected graph, where the nodes of the graph correspond to sentences and edge weights represent the pairwise sentence similarity. In this framework, text segmentation corresponds to a graph partitioning that optimizes the normalized-cut criterion (Shi and Malik, 2000) . This criterion measures both the similarity within each partition and the dissimilarity across different partitions. Thus, our approach moves beyond localized comparisons and takes into account long-range changes in lexical distribution. Our key hypothesis is that global analysis yields more accurate segmentation results than local models.",
"cite_spans": [
{
"start": 379,
"end": 400,
"text": "(Shi and Malik, 2000)",
"ref_id": "BIBREF16"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "We tested our algorithm on a corpus of spoken lectures. Segmentation in this domain is challenging in several respects. Being less structured than written text, lecture material exhibits digressions, disfluencies, and other artifacts of spontaneous communication. In addition, the output of speech recognizers is fraught with high word error rates due to specialized technical vocabulary and lack of in-domain spoken data for training. Finally, pedagogical considerations call for fluent transitions between different topics in a lecture, further complicating the segmentation task.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Our experimental results confirm our hypothesis: considering long-distance lexical dependencies yields substantial gains in segmentation performance. Our graph-theoretic approach compares favorably to state-of-the-art segmentation algorithms and attains results close to the range of human agreement scores. Another attractive prop-erty of the algorithm is its robustness to noise: the accuracy of our algorithm does not deteriorate significantly when applied to speech recognition output.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Most unsupervised algorithms assume that fragments of text with homogeneous lexical distribution correspond to topically coherent segments. Previous research has analyzed various facets of lexical distribution, including lexical weighting, similarity computation, and smoothing (Hearst, 1994; Utiyama and Isahara, 2001; Choi, 2000; Reynar, 1998; Kehagias et al., 2003; Ji and Zha, 2003) .",
"cite_spans": [
{
"start": 278,
"end": 292,
"text": "(Hearst, 1994;",
"ref_id": "BIBREF8"
},
{
"start": 293,
"end": 319,
"text": "Utiyama and Isahara, 2001;",
"ref_id": "BIBREF18"
},
{
"start": 320,
"end": 331,
"text": "Choi, 2000;",
"ref_id": "BIBREF2"
},
{
"start": 332,
"end": 345,
"text": "Reynar, 1998;",
"ref_id": "BIBREF14"
},
{
"start": 346,
"end": 368,
"text": "Kehagias et al., 2003;",
"ref_id": "BIBREF10"
},
{
"start": 369,
"end": 386,
"text": "Ji and Zha, 2003)",
"ref_id": "BIBREF9"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Previous Work",
"sec_num": "2"
},
{
"text": "The focus of our work, however, is on an orthogonal yet fundamental aspect of this analysis -the impact of long-range cohesion dependencies on segmentation performance. In contrast to previous approaches, the homogeneity of a segment is determined not only by the similarity of its words, but also by their relation to words in other segments of the text. We show that optimizing our global objective enables us to detect subtle topical changes.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Previous Work",
"sec_num": "2"
},
{
"text": "Graph-Theoretic Approaches in Vision Segmentation Our work is inspired by minimum-cutbased segmentation algorithms developed for image analysis. Shi and Malik (2000) introduced the normalized-cut criterion and demonstrated its practical benefits for segmenting static images.",
"cite_spans": [
{
"start": 145,
"end": 165,
"text": "Shi and Malik (2000)",
"ref_id": "BIBREF16"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Previous Work",
"sec_num": "2"
},
{
"text": "Our method, however, is not a simple application of the existing approach to a new task. First, in order to make it work in the new linguistic framework, we had to redefine the underlying representation and introduce a variety of smoothing and lexical weighting techniques. Second, the computational techniques for finding the optimal partitioning are also quite different. Since the minimization of the normalized cut is N P -complete in the general case, researchers in vision have to approximate this computation. Fortunately, we can find an exact solution due to the linearity constraint on text segmentation.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Previous Work",
"sec_num": "2"
},
{
"text": "Linguistic research has shown that word repetition in a particular section of a text is a device for creating thematic cohesion (Halliday and Hasan, 1976) , and that changes in the lexical distributions usually signal topic transitions. Figure 1 illustrates these properties in a lecture transcript from an undergraduate Physics class. We use the text Dotplotting representation by (Church, 1993) and plot the cosine similarity scores between every pair of sentences in the text. The intensity of a point (i, j) on the plot indicates the degree to which the i-th sentence in the text is similar to the j-th sentence. The true segment boundaries are denoted by vertical lines. This similarity plot reveals a block structure where true boundaries delimit blocks of text with high inter-sentential similarity. Sentences found in different blocks, on the other hand, tend to exhibit low similarity. Formalizing the Objective Whereas previous unsupervised approaches to segmentation rested on intuitive notions of similarity density, we formalize the objective of text segmentation through cuts on graphs. We aim to jointly maximize the intra-segmental similarity and minimize the similarity between different segments. In other words, we want to find the segmentation with a maximally homogeneous set of segments that are also maxi-mally different from each other.",
"cite_spans": [
{
"start": 128,
"end": 154,
"text": "(Halliday and Hasan, 1976)",
"ref_id": null
},
{
"start": 382,
"end": 396,
"text": "(Church, 1993)",
"ref_id": "BIBREF3"
}
],
"ref_spans": [
{
"start": 237,
"end": 245,
"text": "Figure 1",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Minimum Cut Framework",
"sec_num": "3"
},
{
"text": "u 1 u 2 u 3 u n",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Minimum Cut Framework",
"sec_num": "3"
},
{
"text": "Let G = {V, E} be an undirected, weighted graph, where V is the set of nodes corresponding to sentences in the text and E is the set of weighted edges (See Figure 2) . The edge weights, w(u, v), define a measure of similarity between pairs of nodes u and v, where higher scores indicate higher similarity. Section 4 provides more details on graph construction.",
"cite_spans": [],
"ref_spans": [
{
"start": 156,
"end": 165,
"text": "Figure 2)",
"ref_id": "FIGREF1"
}
],
"eq_spans": [],
"section": "Minimum Cut Framework",
"sec_num": "3"
},
{
"text": "We consider the problem of partitioning the graph into two disjoint sets of nodes A and B. We aim to minimize the cut, which is defined to be the sum of the crossing edges between the two sets of nodes. In other words, we want to split the sentences into two maximally dissimilar classes by choosing A and B to minimize:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Minimum Cut Framework",
"sec_num": "3"
},
{
"text": "cut(A, B) = u\u2208A,v\u2208B w(u, v)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Minimum Cut Framework",
"sec_num": "3"
},
{
"text": "However, we need to ensure that the two partitions are not only maximally different from each other, but also that they are themselves homogeneous by accounting for intra-partition node similarity. We formulate this requirement in the framework of normalized cuts (Shi and Malik, 2000) , where the cut value is normalized by the volume of the corresponding partitions. The volume of the partition is the sum of its edges to the whole graph:",
"cite_spans": [
{
"start": 264,
"end": 285,
"text": "(Shi and Malik, 2000)",
"ref_id": "BIBREF16"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Minimum Cut Framework",
"sec_num": "3"
},
{
"text": "vol(A) = u\u2208A,v\u2208V w(u, v)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Minimum Cut Framework",
"sec_num": "3"
},
{
"text": "The normalized cut criterion (N cut) is then defined as follows:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Minimum Cut Framework",
"sec_num": "3"
},
{
"text": "N cut(A, B) = cut(A, B) vol(A) + cut(A, B) vol(B)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Minimum Cut Framework",
"sec_num": "3"
},
{
"text": "By minimizing this objective we simultaneously minimize the similarity across partitions and maximize the similarity within partitions. This formulation also allows us to decompose the objective into a sum of individual terms, and formulate a dynamic programming solution to the multiway cut problem.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Minimum Cut Framework",
"sec_num": "3"
},
{
"text": "This criterion is naturally extended to a k-way normalized cut:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Minimum Cut Framework",
"sec_num": "3"
},
{
"text": "N cut k (V ) = cut(A1, V \u2212 A1) vol(A1) + . . . + cut(A k , V \u2212 A k ) vol(A k )",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Minimum Cut Framework",
"sec_num": "3"
},
{
"text": "where A 1 . . . A k form a partition of the graph, and V \u2212A k is the set difference between the entire graph and partition k.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Minimum Cut Framework",
"sec_num": "3"
},
{
"text": "Decoding Papadimitriou proved that the problem of minimizing normalized cuts on graphs is N P -complete (Shi and Malik, 2000) . However, in our case, the multi-way cut is constrained to preserve the linearity of the segmentation. By segmentation linearity, we mean that all of the nodes between the leftmost and the rightmost nodes of a particular partition have to belong to that partition. With this constraint, we formulate a dynamic programming algorithm for exactly finding the minimum normalized multiway cut in polynomial time:",
"cite_spans": [
{
"start": 104,
"end": 125,
"text": "(Shi and Malik, 2000)",
"ref_id": "BIBREF16"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Minimum Cut Framework",
"sec_num": "3"
},
{
"text": "C [i, k] = min j<k C [i \u2212 1, j] + cut [A j,k , V \u2212 A j,k ] vol [A j,k ] (1) B [i, k] = argmin j<k C [i \u2212 1, j] + cut [A j,k , V \u2212 A j,k ] vol [A j,k ] (2) s.t. C [0, 1] = 0, C [0, k] = \u221e, 1 < k \u2264 N (3) B [0, k] = 1, 1 \u2264 k \u2264 N (4) C [i, k]",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Minimum Cut Framework",
"sec_num": "3"
},
{
"text": "is the normalized cut value of the optimal segmentation of the first k sentences into i segments. The i-th segment, A j,k , begins at node u j and ends at node u k . B [i, k] is the back-pointer table from which we recover the optimal sequence of segment boundaries. Equations 3 and 4 capture respectively the condition that the normalized cut value of the trivial segmentation of an empty text into one segment is zero and the constraint that the first segment starts with the first node.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Minimum Cut Framework",
"sec_num": "3"
},
{
"text": "The time complexity of the dynamic programming algorithm is O(KN 2 ), where K is the number of partitions and N is the number of nodes in the graph or sentences in the transcript.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Minimum Cut Framework",
"sec_num": "3"
},
{
"text": "Clearly, the performance of our model depends on the underlying representation, the definition of the pairwise similarity function, and various other model parameters. In this section we provide further details on the graph construction process.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Building the Graph",
"sec_num": "4"
},
{
"text": "Preprocessing Before building the graph, we apply standard text preprocessing techniques to the text. We stem words with the Porter stemmer (Porter, 1980) to alleviate the sparsity of word counts through stem equivalence classes. We also remove words matching a prespecified list of stop words.",
"cite_spans": [
{
"start": 140,
"end": 154,
"text": "(Porter, 1980)",
"ref_id": "BIBREF13"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Building the Graph",
"sec_num": "4"
},
{
"text": "Graph Topology As we noted in the previous section, the normalized cut criterion considers long-term similarity relationships between nodes. This effect is achieved by constructing a fullyconnected graph. However, considering all pairwise relations in a long text may be detrimental to segmentation accuracy. Therefore, we discard edges between sentences exceeding a certain threshold distance. This reduction in the graph size also provides us with computational savings.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Building the Graph",
"sec_num": "4"
},
{
"text": "Similarity Computation In computing pairwise sentence similarities, sentences are represented as vectors of word counts. Cosine similarity is commonly used in text segmentation (Hearst, 1994) . To avoid numerical precision issues when summing a series of very small scores, we compute exponentiated cosine similarity scores between pairs of sentence vectors:",
"cite_spans": [
{
"start": 177,
"end": 191,
"text": "(Hearst, 1994)",
"ref_id": "BIBREF8"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Building the Graph",
"sec_num": "4"
},
{
"text": "w(s i , s j ) = e s i \u2022s j ||s i ||\u00d7||s j ||",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Building the Graph",
"sec_num": "4"
},
{
"text": "We further refine our analysis by smoothing the similarity metric. When comparing two sentences, we also take into account similarity between their immediate neighborhoods. The smoothing is achieved by adding counts of words that occur in adjoining sentences to the current sentence feature vector. These counts are weighted in accordance to their distance from the current sentence:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Building the Graph",
"sec_num": "4"
},
{
"text": "s i = i+k j=i e \u2212\u03b1(j\u2212i) s j ,",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Building the Graph",
"sec_num": "4"
},
{
"text": "where s i are vectors of word counts, and \u03b1 is a parameter that controls the degree of smoothing.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Building the Graph",
"sec_num": "4"
},
{
"text": "In the formulation above we use sentences as our nodes. However, we can also represent graph nodes with non-overlapping blocks of words of fixed length. This is desirable, since the lecture transcripts lack sentence boundary markers, and short utterances can skew the cosine similarity scores. The optimal length of the block is tuned on a heldout development set.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Building the Graph",
"sec_num": "4"
},
{
"text": "Lexical Weighting Previous research has shown that weighting schemes play an important role in segmentation performance (Ji and Zha, 2003; Choi et al., 2001 ). Of particular concern are words that may not be common in general English discourse but that occur throughout the text for a particular lecture or subject. For example, in a lecture about support vector machines, the occurrence of the term \"SVM\" is not going to convey a lot of information about the distribution of In order to address this issue, we introduce a variation on the tf-idf scoring scheme used in the information-retrieval literature (Salton and Buckley, 1988) . A transcript is split uniformly into N chunks; each chunk serves as the equivalent of documents in the tf-idf computation. The weights are computed separately for each transcript, since topic and word distributions vary across lectures.",
"cite_spans": [
{
"start": 120,
"end": 138,
"text": "(Ji and Zha, 2003;",
"ref_id": "BIBREF9"
},
{
"start": 139,
"end": 156,
"text": "Choi et al., 2001",
"ref_id": "BIBREF1"
},
{
"start": 607,
"end": 633,
"text": "(Salton and Buckley, 1988)",
"ref_id": "BIBREF15"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Building the Graph",
"sec_num": "4"
},
{
"text": "In this section we present the different corpora used to evaluate our model and provide a brief overview of the evaluation metrics. Next, we describe our human segmentation study on the corpus of spoken lecture data.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Evaluation Set-Up",
"sec_num": "5"
},
{
"text": "A heldout development set of three lectures isused for estimating the optimal word block length for representing nodes, the threshold distances for discarding node edges, the number of uniform chunks for estimating tf-idf lexical weights, the alpha parameter for smoothing, and the length of the smoothing window. We use a simple greedy search procedure for optimizing the parameters.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Parameter Estimation",
"sec_num": "5.1"
},
{
"text": "We evaluate our segmentation algorithm on three sets of data. Two of the datasets we use are new segmentation collections that we have compiled for this study, 1 and the remaining set includes a standard collection previously used for evaluation of segmentation algorithms. Various corpus statistics for the new datasets are presented in Table 1 . Below we briefly describe each corpus.",
"cite_spans": [],
"ref_spans": [
{
"start": 338,
"end": 345,
"text": "Table 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Corpora",
"sec_num": "5.2"
},
{
"text": "Physics Lectures Our first corpus consists of spoken lecture transcripts from an undergraduate Physics class. In contrast to other segmentation datasets, our corpus contains much longer texts. A typical lecture of 90 minutes has 500 to 700 sentences with 8500 words, which corresponds to about 15 pages of raw text. We have access both to manual transcriptions of these lectures and also output from an automatic speech recognition system. The word error rate for the latter is 19.4%, 2 which is representative of state-of-the-art performance on lecture material (Leeuwis et al., 2003) .",
"cite_spans": [
{
"start": 563,
"end": 585,
"text": "(Leeuwis et al., 2003)",
"ref_id": "BIBREF11"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Corpora",
"sec_num": "5.2"
},
{
"text": "The Physics lecture transcript segmentations were produced by the teaching staff of the introductory Physics course at the Massachusetts Institute of Technology. Their objective was to facilitate access to lecture recordings available on the class website. This segmentation conveys the high-level topical structure of the lectures. On average, a lecture was annotated with six segments, and a typical segment corresponds to two pages of a transcript.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Corpora",
"sec_num": "5.2"
},
{
"text": "Artificial Intelligence Lectures Our second lecture corpus differs in subject matter, lecturing style, and segmentation granularity. The graduate Artificial Intelligence class has, on average, twelve segments per lecture, and a typical segment is about half of a page. One segment roughly corresponds to the content of a slide. This time the segmentation was obtained from the lecturer herself. The lecturer went through the transcripts of lecture recordings and segmented the lectures with the objective of making the segments correspond to presentation slides for the lectures.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Corpora",
"sec_num": "5.2"
},
{
"text": "Due to the low recording quality, we were unable to obtain the ASR transcripts for this class. Therefore, we only use manual transcriptions of these lectures.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Corpora",
"sec_num": "5.2"
},
{
"text": "Synthetic Corpus Also as part of our analysis, we used the synthetic corpus created by Choi (2000) which is commonly used in the evaluation of segmentation algorithms. This corpus consists of a set of concatenated segments randomly sampled from the Brown corpus. The length of the segments in this corpus ranges from three to eleven sentences. It is important to note that the lexical transitions in these concatenated texts are very sharp, since the segments come from texts written in widely varying language styles on completely different topics.",
"cite_spans": [
{
"start": 87,
"end": 98,
"text": "Choi (2000)",
"ref_id": "BIBREF2"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Corpora",
"sec_num": "5.2"
},
{
"text": "We use the P k and WindowDiff measures to evaluate our system (Beeferman et al., 1999; Pevzner and Hearst, 2002) . The P k measure estimates the probability that a randomly chosen pair of words within a window of length k words is inconsistently classified. The WindowDiff metric is a variant of the P k measure, which penalizes false positives on an equal basis with near misses.",
"cite_spans": [
{
"start": 62,
"end": 86,
"text": "(Beeferman et al., 1999;",
"ref_id": "BIBREF0"
},
{
"start": 87,
"end": 112,
"text": "Pevzner and Hearst, 2002)",
"ref_id": "BIBREF12"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Evaluation Metric",
"sec_num": "5.3"
},
{
"text": "Both of these metrics are defined with respect to the average segment length of texts and exhibit high variability on real data. We follow Choi (2000) and compute the mean segment length used in determining the parameter k on each reference text separately.",
"cite_spans": [
{
"start": 139,
"end": 150,
"text": "Choi (2000)",
"ref_id": "BIBREF2"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Evaluation Metric",
"sec_num": "5.3"
},
{
"text": "We also plot the Receiver Operating Characteristic (ROC) curve to gauge performance at a finer level of discrimination (Swets, 1988) . The ROC plot is the plot of the true positive rate against the false positive rate for various settings of a decision criterion. In our case, the true positive rate is the fraction of boundaries correctly classified, and the false positive rate is the fraction of non-boundary positions incorrectly classified as boundaries. In computing the true and false positive rates, we vary the threshold distance to the true boundary within which a hypothesized boundary is considered correct. Larger areas under the ROC curve of a classifier indicate better discriminative performance.",
"cite_spans": [
{
"start": 119,
"end": 132,
"text": "(Swets, 1988)",
"ref_id": "BIBREF17"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Evaluation Metric",
"sec_num": "5.3"
},
{
"text": "Spoken lectures are very different in style from other corpora used in human segmentation studies (Hearst, 1994; Galley et al., 2003) . We are interested in analyzing human performance on a corpus of lecture transcripts with much longer texts and a less clear-cut concept of a sub-topic. We define a segment to be a sub-topic that signals a prominent shift in subject matter. Disregarding this sub-topic change would impair the high-level understanding of the structure and the content of the lecture.",
"cite_spans": [
{
"start": 98,
"end": 112,
"text": "(Hearst, 1994;",
"ref_id": "BIBREF8"
},
{
"start": 113,
"end": 133,
"text": "Galley et al., 2003)",
"ref_id": "BIBREF4"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Human Segmentation Study",
"sec_num": "5.4"
},
{
"text": "As part of our human segmentation analysis, we asked three annotators to segment the Physics lecture corpus. These annotators had taken the class in the past and were familiar with the subject matter under consideration. We wrote a detailed instruction manual for the task, with annotation guidelines for the most part following the model used by Gruenstein et al. (2005) . The annotators were instructed to segment at a level of granularity O A B C MEAN SEG. COUNT 6.6 8.9 18.4 13.8 MEAN SEG. LENGTH 69.4 51.5 24.9 33.2 SEG. LENGTH DEV. 39.6 37.4 34.5 39.4 that would identify most of the prominent topical transitions necessary for a summary of the lecture. The annotators used the NOMOS annotation software toolkit, developed for meeting segmentation (Gruenstein et al., 2005) . They were provided with recorded audio of the lectures and the corresponding text transcriptions. We intentionally did not provide the subjects with the target number of boundaries, since we wanted to see if the annotators would converge on a common segmentation granularity. Table 2 presents the annotator segmentation statistics. We see two classes of segmentation granularities. The original reference (O) and annotator A segmented at a coarse level with an average of 6.6 and 8.9 segments per lecture, respectively. Annotators B and C operated at much finer levels of discrimination with 18.4 and 13.8 segments per lecture on average. We conclude that multiple levels of granularity are acceptable in spoken lecture segmentation. This is expected given the length of the lectures and varying human judgments in selecting relevant topical content.",
"cite_spans": [
{
"start": 347,
"end": 371,
"text": "Gruenstein et al. (2005)",
"ref_id": "BIBREF6"
},
{
"start": 754,
"end": 779,
"text": "(Gruenstein et al., 2005)",
"ref_id": "BIBREF6"
}
],
"ref_spans": [
{
"start": 1058,
"end": 1065,
"text": "Table 2",
"ref_id": "TABREF1"
}
],
"eq_spans": [],
"section": "Human Segmentation Study",
"sec_num": "5.4"
},
{
"text": "Following previous studies, we quantify the level of annotator agreement with the P k measure (Gruenstein et al., 2005 ). 3 Table 3 shows the annotator agreement scores between different pairs of annotators. P k measures ranged from 0.24 and 0.42. We observe greater consistency at similar levels of granularity, and less so across the two Table 4 : Edges between nodes separated beyond a certain threshold distance are removed.",
"cite_spans": [
{
"start": 94,
"end": 118,
"text": "(Gruenstein et al., 2005",
"ref_id": "BIBREF6"
}
],
"ref_spans": [
{
"start": 124,
"end": 131,
"text": "Table 3",
"ref_id": "TABREF2"
},
{
"start": 340,
"end": 347,
"text": "Table 4",
"ref_id": null
}
],
"eq_spans": [],
"section": "Human Segmentation Study",
"sec_num": "5.4"
},
{
"text": "classes. Note that annotator A operated at a level of granularity consistent with the original reference segmentation. Hence, the 0.24 P k measure score serves as the benchmark with which we can compare the results attained by segmentation algorithms on the Physics lecture data.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Human Segmentation Study",
"sec_num": "5.4"
},
{
"text": "As an additional point of reference we note that the uniform and random baseline segmentations attain 0.469 and 0.493 P k measure, respectively, on the Physics lecture set. ",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Human Segmentation Study",
"sec_num": "5.4"
},
{
"text": "We first determine the impact of long-range pairwise similarity dependencies on segmentation performance. Our Table 5 : Performance analysis of different algorithms using the P k and WindowDiff measures, with three lectures heldout for development.",
"cite_spans": [],
"ref_spans": [
{
"start": 110,
"end": 117,
"text": "Table 5",
"ref_id": null
}
],
"eq_spans": [],
"section": "Benefits of global analysis",
"sec_num": null
},
{
"text": "key hypothesis is that considering long-distance lexical relations contributes to the effectiveness of the algorithm. To test this hypothesis, we discard edges between nodes that are more than a certain number of sentences apart. We test the system on a range of data sets, including the Physics and AI lectures and the synthetic corpus created by Choi (2000) . We also include segmentation results on Physics ASR transcripts.",
"cite_spans": [
{
"start": 348,
"end": 359,
"text": "Choi (2000)",
"ref_id": "BIBREF2"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Benefits of global analysis",
"sec_num": null
},
{
"text": "The results in Table 4 confirm our hypothesistaking into account non-local lexical dependencies helps across different domains. On manually transcribed Physics lecture data, for example, the algorithm yields 0.394 P k measure when taking into account edges separated by up to ten sentences. When dependencies up to a hundred sentences are considered, the algorithm yields a 25% reduction in P k measure. Figure 3 shows the ROC plot for the segmentation of the Physics lecture data with different cutoff parameters, again demonstrating clear gains attained by employing longrange dependencies. As Table 4 shows, the improvement is consistent across all lecture datasets. We note, however, that after some point increasing the threshold degrades performance, because it introduces too many spurious dependencies (see the last column of Table 4 ). The speaker will occasionally return to a topic described at the beginning of the lecture, and this will bias the algorithm to put the segment boundary closer to the end of the lecture.",
"cite_spans": [],
"ref_spans": [
{
"start": 15,
"end": 22,
"text": "Table 4",
"ref_id": null
},
{
"start": 404,
"end": 412,
"text": "Figure 3",
"ref_id": "FIGREF4"
},
{
"start": 596,
"end": 603,
"text": "Table 4",
"ref_id": null
},
{
"start": 834,
"end": 841,
"text": "Table 4",
"ref_id": null
}
],
"eq_spans": [],
"section": "Benefits of global analysis",
"sec_num": null
},
{
"text": "Long-range dependencies do not improve the performance on the synthetic dataset. This is expected since the segments in the synthetic dataset are randomly selected from widely-varying documents in the Brown corpus, even spanning different genres of written language. So, effectively, there are no genuine long-range dependencies that can be exploited by the algorithm.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Benefits of global analysis",
"sec_num": null
},
{
"text": "Comparison with local dependency models We compare our system with the state-of-the-art similarity-based segmentation system developed by Choi (2000) . We use the publicly available implementation of the system and optimize the system on a range of mask-sizes and different parameter settings described in (Choi, 2000) on a heldout development set of three lectures. To control for segmentation granularity, we specify the number of segments in the reference (\"O\") segmentation for both our system and the baseline. Table 5 shows that the Minimum Cut algorithm consistently outperforms the similarity-based baseline on all the lecture datasets. We attribute this gain to the presence of more attenuated topic transitions in spoken language. Since spoken language is more spontaneous and less structured than written language, the speaker needs to keep the listener abreast of the changes in topic content by introducing subtle cues and references to prior topics in the course of topical transitions. Non-local dependencies help to elucidate shifts in focus, because the strength of a particular transition is measured with respect to other local and long-distance contextual discourse relationships.",
"cite_spans": [
{
"start": 138,
"end": 149,
"text": "Choi (2000)",
"ref_id": "BIBREF2"
},
{
"start": 306,
"end": 318,
"text": "(Choi, 2000)",
"ref_id": "BIBREF2"
}
],
"ref_spans": [
{
"start": 516,
"end": 523,
"text": "Table 5",
"ref_id": null
}
],
"eq_spans": [],
"section": "Benefits of global analysis",
"sec_num": null
},
{
"text": "Our system does not outperform Choi's algorithm on the synthetic data. This again can be attributed to the discrepancy in distributional properties of the synthetic corpus which lacks coherence in its thematic shifts and the lecture corpus of spontaneous speech with smooth distributional variations. We also note that we did not try to adjust our model to optimize its performance on the synthetic data. The smoothing method developed for lecture segmentation may not be appropriate for short segments ranging from three to eleven sentences that constitute the synthetic set.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Benefits of global analysis",
"sec_num": null
},
{
"text": "We also compared our method with another state-of-the-art algorithm which does not explicitly rely on pairwise similarity analysis. This algorithm (Utiyama and Isahara, 2001 ) (UI) computes the optimal segmentation by estimating changes in the language model predictions over different partitions. We used the publicly available implemen-tation of the system that does not require parameter tuning on a heldout development set.",
"cite_spans": [
{
"start": 147,
"end": 173,
"text": "(Utiyama and Isahara, 2001",
"ref_id": "BIBREF18"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Benefits of global analysis",
"sec_num": null
},
{
"text": "Again, our method achieves favorable performance on a range of lecture data sets (See Table 5), and both algorithms attain results close to the range of human agreement scores. The attractive feature of our algorithm, however, is robustness to recognition errors -testing it on the ASR transcripts caused only 7.8% relative increase in P k measure (from 0.298 to 0.322), compared to a 13.5% relative increase for the UI system. We attribute this feature to the fact that the model is less dependent on individual recognition errors, which have a detrimental effect on the local segment language modeling probability estimates for the UI system. The block-level similarity function is not as sensitive to individual word errors, because the partition volume normalization factor dampens their overall effect on the derived models.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Benefits of global analysis",
"sec_num": null
},
{
"text": "In this paper we studied the impact of long-range dependencies on the accuracy of text segmentation. We modeled text segmentation as a graphpartitioning task aiming to simultaneously optimize the total similarity within each segment and dissimilarity across various segments. We showed that global analysis of lexical distribution improves the segmentation accuracy and is robust in the presence of recognition errors. Combining global analysis with advanced methods for smoothing (Ji and Zha, 2003) and weighting could further boost the segmentation performance.",
"cite_spans": [
{
"start": 481,
"end": 499,
"text": "(Ji and Zha, 2003)",
"ref_id": "BIBREF9"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions",
"sec_num": "7"
},
{
"text": "Our current implementation does not automatically determine the granularity of a resulting segmentation. This issue has been explored in the past (Ji and Zha, 2003; Utiyama and Isahara, 2001) , and we will explore the existing strategies in our framework. We believe that the algorithm has to produce segmentations for various levels of granularity, depending on the needs of the application that employs it.",
"cite_spans": [
{
"start": 146,
"end": 164,
"text": "(Ji and Zha, 2003;",
"ref_id": "BIBREF9"
},
{
"start": 165,
"end": 191,
"text": "Utiyama and Isahara, 2001)",
"ref_id": "BIBREF18"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions",
"sec_num": "7"
},
{
"text": "Our ultimate goal is to automatically generate tables of content for lectures. We plan to investigate strategies for generating titles that will succinctly describe the content of each segment. We will explore how the interaction between the generation and segmentation components can improve the performance of such a system as a whole.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions",
"sec_num": "7"
},
{
"text": "Our materials are publicly available at http://www. csail.mit.edu/\u02dcigorm/acl06.html",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "A speaker-dependent model of the lecturer was trained on 38 hours of lectures from other courses using the SUM-MIT segment-based Speech Recognizer(Glass, 2003).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "Kappa measure would not be the appropriate measure in this case, because it is not sensitive to near misses, and we cannot make the required independence assumption on the placement of boundaries.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [
{
"text": "The authors acknowledge the support of the National Science Foundation (CAREER grant IIS-0448168, grant IIS-0415865, and the NSF Graduate Fellowship). Any opinions, findings, conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. We would like to thank Masao Utiyama for providing us with an implementation of his segmentation system and Alex Gruenstein for assisting us with the NOMOS toolkit. We are grateful to David Karger for an illuminating discussion on the Minimum Cut algorithm. We also would like to acknowledge the MIT NLP and Speech Groups, the three annotators, and the three anonymous reviewers for valuable comments, suggestions, and help.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgements",
"sec_num": "8"
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Statistical models for text segmentation",
"authors": [
{
"first": "D",
"middle": [],
"last": "Beeferman",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Berger",
"suffix": ""
},
{
"first": "J",
"middle": [
"D"
],
"last": "Lafferty",
"suffix": ""
}
],
"year": 1999,
"venue": "Machine Learning",
"volume": "34",
"issue": "",
"pages": "177--210",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "D. Beeferman, A. Berger, J. D. Lafferty. 1999. Statistical models for text segmentation. Machine Learning, 34(1- 3):177-210.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Latent semantic analysis for text segmentation",
"authors": [
{
"first": "F",
"middle": [],
"last": "Choi",
"suffix": ""
},
{
"first": "P",
"middle": [],
"last": "Wiemer-Hastings",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Moore",
"suffix": ""
}
],
"year": 2001,
"venue": "Proceedings of EMNLP",
"volume": "",
"issue": "",
"pages": "109--117",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "F. Choi, P. Wiemer-Hastings, J. Moore. 2001. Latent se- mantic analysis for text segmentation. In Proceedings of EMNLP, 109-117.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Advances in domain independent linear text segmentation",
"authors": [
{
"first": "F",
"middle": [
"Y Y"
],
"last": "Choi",
"suffix": ""
}
],
"year": 2000,
"venue": "Proceedings of the NAACL",
"volume": "",
"issue": "",
"pages": "26--33",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "F. Y. Y. Choi. 2000. Advances in domain independent linear text segmentation. In Proceedings of the NAACL, 26-33.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Char align: A program for aligning parallel texts at the character level",
"authors": [
{
"first": "K",
"middle": [
"W"
],
"last": "Church",
"suffix": ""
}
],
"year": 1993,
"venue": "Proceedings of the ACL",
"volume": "",
"issue": "",
"pages": "1--8",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "K. W. Church. 1993. Char align: A program for aligning parallel texts at the character level. In Proceedings of the ACL, 1-8.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Discourse segmentation of multi-party conversation",
"authors": [
{
"first": "M",
"middle": [],
"last": "Galley",
"suffix": ""
},
{
"first": "K",
"middle": [],
"last": "Mckeown",
"suffix": ""
},
{
"first": "E",
"middle": [],
"last": "Fosler-Lussier",
"suffix": ""
},
{
"first": "H",
"middle": [],
"last": "Jing",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of the ACL",
"volume": "",
"issue": "",
"pages": "562--569",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "M. Galley, K. McKeown, E. Fosler-Lussier, H. Jing. 2003. Discourse segmentation of multi-party conversation. In Proceedings of the ACL, 562-569.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "A probabilistic framework for segmentbased speech recognition",
"authors": [
{
"first": "J",
"middle": [
"R"
],
"last": "Glass",
"suffix": ""
}
],
"year": 2003,
"venue": "Computer Speech and Language",
"volume": "17",
"issue": "2-3",
"pages": "137--152",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "J. R. Glass. 2003. A probabilistic framework for segment- based speech recognition. Computer Speech and Lan- guage, 17(2-3):137-152.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Meeting structure annotation: Data and tools",
"authors": [
{
"first": "A",
"middle": [],
"last": "Gruenstein",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Niekrasz",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Purver",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of the SIGdial Workshop on Discourse and Dialogue",
"volume": "",
"issue": "",
"pages": "117--127",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "A. Gruenstein, J. Niekrasz, M. Purver. 2005. Meeting struc- ture annotation: Data and tools. In Proceedings of the SIGdial Workshop on Discourse and Dialogue, 117-127.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Multi-paragraph segmentation of expository text",
"authors": [
{
"first": "M",
"middle": [],
"last": "Hearst",
"suffix": ""
}
],
"year": 1994,
"venue": "Proceedings of the ACL",
"volume": "",
"issue": "",
"pages": "9--16",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "M. Hearst. 1994. Multi-paragraph segmentation of exposi- tory text. In Proceedings of the ACL, 9-16.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Domain-independent text segmentation using anisotropic diffusion and dynamic programming",
"authors": [
{
"first": "X",
"middle": [],
"last": "Ji",
"suffix": ""
},
{
"first": "H",
"middle": [],
"last": "Zha",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of SIGIR",
"volume": "",
"issue": "",
"pages": "322--329",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "X. Ji, H. Zha. 2003. Domain-independent text segmentation using anisotropic diffusion and dynamic programming. In Proceedings of SIGIR, 322-329.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Linear text segmentation using a dynamic programming algorithm",
"authors": [
{
"first": "A",
"middle": [],
"last": "Kehagias",
"suffix": ""
},
{
"first": "P",
"middle": [],
"last": "Fragkou",
"suffix": ""
},
{
"first": "V",
"middle": [],
"last": "Petridis",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of the EACL",
"volume": "",
"issue": "",
"pages": "171--178",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "A. Kehagias, P. Fragkou, V. Petridis. 2003. Linear text seg- mentation using a dynamic programming algorithm. In Proceedings of the EACL, 171-178.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Language modeling and transcription of the ted corpus lectures",
"authors": [
{
"first": "E",
"middle": [],
"last": "Leeuwis",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Federico",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Cettolo",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of ICASSP",
"volume": "",
"issue": "",
"pages": "232--235",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "E. Leeuwis, M. Federico, M. Cettolo. 2003. Language mod- eling and transcription of the ted corpus lectures. In Pro- ceedings of ICASSP, 232-235.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "A critique and improvement of an evaluation metric for text segmentation",
"authors": [
{
"first": "L",
"middle": [],
"last": "Pevzner",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Hearst",
"suffix": ""
}
],
"year": 2002,
"venue": "Computational Linguistics",
"volume": "28",
"issue": "1",
"pages": "19--36",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "L. Pevzner, M. Hearst. 2002. A critique and improvement of an evaluation metric for text segmentation. Computa- tional Linguistics, 28(1):pp. 19-36.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "An algorithm for suffix stripping",
"authors": [
{
"first": "M",
"middle": [
"F"
],
"last": "Porter",
"suffix": ""
}
],
"year": 1980,
"venue": "Program",
"volume": "14",
"issue": "3",
"pages": "130--137",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "M. F. Porter. 1980. An algorithm for suffix stripping. Pro- gram, 14(3):130-137.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Topic segmentation: Algorithms and applications",
"authors": [
{
"first": "J",
"middle": [],
"last": "Reynar",
"suffix": ""
}
],
"year": 1998,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "J. Reynar. 1998. Topic segmentation: Algorithms and appli- cations. Ph.D. thesis, University of Pennsylvania.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Term weighting approaches in automatic text retrieval",
"authors": [
{
"first": "G",
"middle": [],
"last": "Salton",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Buckley",
"suffix": ""
}
],
"year": 1988,
"venue": "Information Processing and Management",
"volume": "24",
"issue": "5",
"pages": "513--523",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "G. Salton, C. Buckley. 1988. Term weighting approaches in automatic text retrieval. Information Processing and Management, 24(5):513-523.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Normalized cuts and image segmentation",
"authors": [
{
"first": "J",
"middle": [],
"last": "Shi",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Malik",
"suffix": ""
}
],
"year": 2000,
"venue": "IEEE Transactions on Pattern Analysis and Machine Intelligence",
"volume": "22",
"issue": "8",
"pages": "888--905",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "J. Shi, J. Malik. 2000. Normalized cuts and image segmenta- tion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):888-905.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Measuring the accuracy of diagnostic systems",
"authors": [
{
"first": "J",
"middle": [],
"last": "Swets",
"suffix": ""
}
],
"year": 1988,
"venue": "Science",
"volume": "240",
"issue": "4857",
"pages": "1285--1293",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "J. Swets. 1988. Measuring the accuracy of diagnostic sys- tems. Science, 240(4857):1285-1293.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "A statistical model for domain-independent text segmentation",
"authors": [
{
"first": "M",
"middle": [],
"last": "Utiyama",
"suffix": ""
},
{
"first": "H",
"middle": [],
"last": "Isahara",
"suffix": ""
}
],
"year": 2001,
"venue": "Proceedings of the ACL",
"volume": "",
"issue": "",
"pages": "499--506",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "M. Utiyama, H. Isahara. 2001. A statistical model for domain-independent text segmentation. In Proceedings of the ACL, 499-506.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"uris": null,
"text": "Sentence similarity plot for a Physics lecture, with vertical lines indicating true segment boundaries.",
"num": null,
"type_str": "figure"
},
"FIGREF1": {
"uris": null,
"text": "Graph-based Representation of Text",
"num": null,
"type_str": "figure"
},
"FIGREF4": {
"uris": null,
"text": "ROC plot for the Minimum Cut Segmenter on thirty Physics Lectures, with edge cutoffs set at five and hundred sentences.",
"num": null,
"type_str": "figure"
},
"TABREF1": {
"text": "Annotator Segmentation Statistics for the first ten Physics lectures.",
"html": null,
"content": "<table><tr><td colspan=\"2\">REF/HYP O</td><td>A</td><td>B</td><td>C</td></tr><tr><td>O</td><td>0</td><td colspan=\"3\">0.243 0.418 0.312</td></tr><tr><td>A</td><td colspan=\"2\">0.219 0</td><td colspan=\"2\">0.400 0.355</td></tr><tr><td>B</td><td colspan=\"3\">0.314 0.337 0</td><td>0.332</td></tr><tr><td>C</td><td colspan=\"4\">0.260 0.296 0.370 0</td></tr></table>",
"num": null,
"type_str": "table"
},
"TABREF2": {
"text": "P k annotation agreement between different pairs of annotators.",
"html": null,
"content": "<table/>",
"num": null,
"type_str": "table"
}
}
}
} |