File size: 69,080 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 | {
"paper_id": "F14-2012",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T10:22:17.283295Z"
},
"title": "Centrality Measures for Non-Contextual Graph-Based Unsupervised Single Document Keyword Extraction",
"authors": [
{
"first": "Natalie",
"middle": [],
"last": "Schluter",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Malm\u00f6 University",
"location": {
"settlement": "Malm\u00f6",
"country": "Sweden"
}
},
"email": "natalie.schluter@mah.se"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "The manner in which keywords fulfill the role of being central to a document is frustratingly still an open question. In this paper, we hope to shed some light on the essence of keywords in scientific articles and thereby motivate the graph-based approach to keyword extraction. We identify the document model captured by the text graph generated as input to a number of centrality metrics, and overview what these metrics say about keywords. In doing so, we achieve state-of-the-art results in unsupervised non-contextual single document keyword extraction.",
"pdf_parse": {
"paper_id": "F14-2012",
"_pdf_hash": "",
"abstract": [
{
"text": "The manner in which keywords fulfill the role of being central to a document is frustratingly still an open question. In this paper, we hope to shed some light on the essence of keywords in scientific articles and thereby motivate the graph-based approach to keyword extraction. We identify the document model captured by the text graph generated as input to a number of centrality metrics, and overview what these metrics say about keywords. In doing so, we achieve state-of-the-art results in unsupervised non-contextual single document keyword extraction.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Researchers are far from reaching a consensus about what a keyword extracted from a document actually is, which may provide part of the explanation for why the keyword extraction task is such a challenge. Some characterisations of keywords are that, with respect to the document at hand, they are important or significant (Mihalcea & Tarau, 2004; Liu et al., 2009a; Wan & Xiao, 2008 ), salient (Wan et al., 2007 , or (less directly) central, and that somehow they fulfill the role of little summaries for the document and represent the document. But how keywords fulfill these characteristics, is a question that is still wide open and building a system for a task that is not well-defined is difficult, if not unwise. However, the important role of keywords in tasks such as document indexing for search engines, summarisation, clustering and classification, make this (currently) ill-defined research necessary.",
"cite_spans": [
{
"start": 322,
"end": 346,
"text": "(Mihalcea & Tarau, 2004;",
"ref_id": "BIBREF11"
},
{
"start": 347,
"end": 365,
"text": "Liu et al., 2009a;",
"ref_id": "BIBREF8"
},
{
"start": 366,
"end": 382,
"text": "Wan & Xiao, 2008",
"ref_id": "BIBREF15"
},
{
"start": 383,
"end": 411,
"text": "), salient (Wan et al., 2007",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction and Previous Work",
"sec_num": "1"
},
{
"text": "In this paper, we present a study on the essence of keywords in scientific articles and thereby motivate the graph-based approach to keyword extraction. We identify the document model captured by the text graph generated as input to a number of centrality metrics, and overview what these metrics say about keywords. In doing so, we achieve state-ofthe-art results in non-contextual single document keyword extraction for the Inspec corpus, an NDCG score of 0.07578 ; we can affirm this, because the systems we compare here in terms of NDCG include re-implementations of the previous state-of-the-art.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction and Previous Work",
"sec_num": "1"
},
{
"text": "We identify two broad types of single document keyword extraction (SDKE). Contextual SDKE makes use of the document set to which the relevant document belongs, and in which there are similar documents ; other information outside of the document set may also be used in some types of contextual SDKE. Non-contextual SDKE makes use of only the relevant document with no other information. The latter does not necessarily make the assumption of independence of documents in general. Non-contextual SDKE is actually important for the case of isolated documents (not part of a document set), as well as for documents for which relevant supplementary information may be non-existent or unreliable. In addition, the study of non-contextual SDKE is a study of baselines in keyword extraction : before constructing complex methods using more information, it is important to understand what we can achieve with the document alone. This paper presents a study of graph-based unsupervised non-contextual SDKE. Recent studies by Mihalcea & Tarau (2004) (which was the original approach), Rose et al. Rose et al. (2010), Litvak, Last & Litvak et al. (2013) , and Litvak & Last (2008) , which also represent the state-of-the-art for the unsupervised version of this task, all use graph-based methods. The superior method (in (Rose et al., 2010), rediscovered in (Litvak et al., 2013) ) of the three simply extracts the vertex degree ; the other methods apply more complex algorithms (PageRank (Brin & Page, 1998) is applied in (Mihalcea & Tarau, 2004; Liu et al., 2010; Zhao et al., 2011) and HITS (Kleinberg, 1999) is applied in (Litvak & Last, 2008) ). What all three methods have in common, though they do not state that this is their explicit intention, is that they exploit measures of centrality of the graph. This paper aims to make these centrality measures explicit and study their appropriateness for the task. Our work will be comparable to that of Mihalcea & Tarau (2004) and Litvak et al. (2013) , who also worked on unsupervised non-contextual single document keyword extraction for the same dataset, with the latter presenting the previous state-of-the-art. We carry out experiments on the test set from the Inspec abstract corpus (Hulth, 2003) consisting of 500 abstracts for scientific articles, along with the uncontrolled corresponding keywords.",
"cite_spans": [
{
"start": 1107,
"end": 1142,
"text": "Litvak, Last & Litvak et al. (2013)",
"ref_id": "BIBREF7"
},
{
"start": 1149,
"end": 1169,
"text": "Litvak & Last (2008)",
"ref_id": "BIBREF6"
},
{
"start": 1347,
"end": 1368,
"text": "(Litvak et al., 2013)",
"ref_id": "BIBREF7"
},
{
"start": 1478,
"end": 1497,
"text": "(Brin & Page, 1998)",
"ref_id": "BIBREF2"
},
{
"start": 1512,
"end": 1536,
"text": "(Mihalcea & Tarau, 2004;",
"ref_id": "BIBREF11"
},
{
"start": 1537,
"end": 1554,
"text": "Liu et al., 2010;",
"ref_id": "BIBREF9"
},
{
"start": 1555,
"end": 1573,
"text": "Zhao et al., 2011)",
"ref_id": "BIBREF18"
},
{
"start": 1583,
"end": 1600,
"text": "(Kleinberg, 1999)",
"ref_id": "BIBREF5"
},
{
"start": 1615,
"end": 1636,
"text": "(Litvak & Last, 2008)",
"ref_id": "BIBREF6"
},
{
"start": 1945,
"end": 1968,
"text": "Mihalcea & Tarau (2004)",
"ref_id": "BIBREF11"
},
{
"start": 1973,
"end": 1993,
"text": "Litvak et al. (2013)",
"ref_id": "BIBREF7"
},
{
"start": 2231,
"end": 2244,
"text": "(Hulth, 2003)",
"ref_id": "BIBREF4"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction and Previous Work",
"sec_num": "1"
},
{
"text": "We consider seven well-known measures of centrality, which, following (Borgatti & Everett, 2006) are distributed among the three categories : degree-like centrality, closeness-like centrality, and betweenness-like centrality. In doing so, we hope to either provide motivation, both conceptually (and/or mathematically) and empirically with respect to the appropriateness of these centrality measures. We also give the first non-results oriented motivation for the use of a graph representation of document that provides a basis for which these centrality measures are of interest.",
"cite_spans": [
{
"start": 70,
"end": 96,
"text": "(Borgatti & Everett, 2006)",
"ref_id": "BIBREF1"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction and Previous Work",
"sec_num": "1"
},
{
"text": "We begin in Section 2 by motivating the document graph model. Then we turn our discussion to centrality measures (Section 3). Finally, we present the experimental results and discussion (Sections 4 and 5).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction and Previous Work",
"sec_num": "1"
},
{
"text": "2 What is the graph ?",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction and Previous Work",
"sec_num": "1"
},
{
"text": "The graph model in previous work. In non-contextual SDKE, the main motivation for attempting to model the text of a document as a graph is for the use of some ranking algorithm over the textual units of the entire graph, based on some notion of centrality, where the relationships modelled between these units (i.e., the edges or directed edges) are co-occurrence relationships. (Litvak & Last, 2008) and (Litvak et al., 2013) posit that this reflects linguistic syntax. Certainly, specific linear order textual relations can result from linguistic syntax, to various degrees depending on the language in question, and to a large degree in English specifically. However, it is unclear why syntax alone should be the motivation for the creation of a text graph and its input into a ranking algorithm. What specifically is the role of syntax in determining the most important textual units of a document ? (Mihalcea & Tarau, 2004) , on the other hand, argue that the edges represent connections between concepts (approximated by text units), in terms of their cohesiveness for building up a conceptual context \"web\" in which a human would understand the text at hand ; in this context web, they explain, some units are more important than others, and they are detectible by their connections with other important concepts, interpreted as \"recommendations\". They also introduce the notion of a text surfer, which we interpret to be the reader, over this concept web (PageRank with the concept web as input), as the algorithm for detecting some inherent ranking of units in this web. From these notions, two questions immediately arise. How does a concept (word) recommend another word from mere co-occurrence ? What does it mean for a word to recommend another word ? For the case of the internet, it is clear how these (directed) links form immediate recommendations for given topics. However, this seems to not be as clear in the case of texts.",
"cite_spans": [
{
"start": 379,
"end": 400,
"text": "(Litvak & Last, 2008)",
"ref_id": "BIBREF6"
},
{
"start": 405,
"end": 426,
"text": "(Litvak et al., 2013)",
"ref_id": "BIBREF7"
},
{
"start": 904,
"end": 928,
"text": "(Mihalcea & Tarau, 2004)",
"ref_id": "BIBREF11"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction and Previous Work",
"sec_num": "1"
},
{
"text": "Sometimes the edges are directed (as in, for example, (Litvak & Last, 2008; Litvak et al., 2013) ) and sometimes undirected (as in, for example (Mihalcea & Tarau, 2004) ). However, no motivation is given for directed-ness of the relationships based on the nature of the object modelled, over a results related argument from the experiments reported in these studies (they tried both and one type yielded best results), or from some other study (someone else tried both and one type yielded best results).",
"cite_spans": [
{
"start": 54,
"end": 75,
"text": "(Litvak & Last, 2008;",
"ref_id": "BIBREF6"
},
{
"start": 76,
"end": 96,
"text": "Litvak et al., 2013)",
"ref_id": "BIBREF7"
},
{
"start": 144,
"end": 168,
"text": "(Mihalcea & Tarau, 2004)",
"ref_id": "BIBREF11"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction and Previous Work",
"sec_num": "1"
},
{
"text": "The graph model in this work : concept-building in communication. In this work, we model text as an undirected graph, where vertices are words appearing in the text and edges model text-linear relationships between words (i.e., that they are beside each other in the text) ; vertices representing semantically rich words (approximated here by non-stopwords) are collapsed into a single vertex if they are of the same form and part-of-speech. We follow (Mihalcea & Tarau, 2004) , in considering the words of the text as approximations of concepts, but our model motivation differs in two very important respects, as we will now explain.",
"cite_spans": [
{
"start": 452,
"end": 476,
"text": "(Mihalcea & Tarau, 2004)",
"ref_id": "BIBREF11"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction and Previous Work",
"sec_num": "1"
},
{
"text": "One essential difference is that we view text from the point of view of synthesis (text creation) rather than analysis (reading). We posit that (at least) for scientific text, discussing a topic efficiently requires concept coordination. In generating scientific text on a given topic (or given related topics), the \"author\" may require other concepts to regularly support the discussion (for example, definitions or explanations). We assume that the author is communicating in the most efficient manner possible, and that supporting concepts are named only when absolutely necessary. Moreover, we make the observation that in supporting or defining a concept, textual mention of a topic concept and supporting concepts should occur rather close to each other, in terms of the linear order of concepts (words) in the texts. We therefore approximate these concept support relations by co-occurrence relations, but recognise that these relations are essentially undirected : there is no clear order that should be observed between topic concepts and supporting concepts within a single sentence (or over several sentences for that matter). Note that the network is not the meaning of the documentation ; rather it is a representation of its construction. Flow through the concept network is seen as communicative|concept-building on the part of the author for the reader.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction and Previous Work",
"sec_num": "1"
},
{
"text": "Given this graph, we want to find the most \"central\" concepts/words and propose these as keywords for the document. However, several notions of centrality can now be applied. A concept can be considered important, because it 1. has first-hand access to many concepts (degree centrality), 2. is very close (and therefore is indirectly used as support) in the network to many distinct concepts (closeness centrality), or 3. is between many concepts and therefore might be often traversed (expressed) in order to build a discussion starting at one concept and ending at another (between-ness centrality). We examine all these types of centralities using conventional deterministic measures for them, to present how the different centrality measures represent the reality of the gold keyword sets.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction and Previous Work",
"sec_num": "1"
},
{
"text": "Generating the keyword graph. We first carry out sentence detection, tokenisation and part-of-speech tagging on the corpus, using the Stanford POS Tagger (Toutanova et al., 2003) . We remove all punctuation from individual sentences. However our sentences are segmented (unlike, for example in (Mihalcea & Tarau, 2004)) ; so we take sentence terminating punctuation into account (like (Litvak & Last, 2008) ). We also use a stop-word list to create two different types of graphs, both of which can be constructed in time linear in the length of the document.",
"cite_spans": [
{
"start": 154,
"end": 178,
"text": "(Toutanova et al., 2003)",
"ref_id": "BIBREF14"
},
{
"start": 385,
"end": 406,
"text": "(Litvak & Last, 2008)",
"ref_id": "BIBREF6"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction and Previous Work",
"sec_num": "1"
},
{
"text": "1. The reduced adjacency graph, which is constructed from the text stripped of stop-words. An edge between two words is added to the graph if these two words are adjacent in the text. Two word vertices decorated with words of the same form and (first letter of) part-of-speech are collapsed into a single vertex. This method follows that of (Liu et al., 2009a) . Note that we do not filter out words of a certain part-of-speech tag, unlike (Liu et al., 2009b; Mihalcea & Tarau, 2004; Litvak & Last, 2008 ) as a preprocessing step. 2. The full adjacency graph, which is constructed from the full unstriped text. This time, however, word vertices that are not stop-words of the same form and part-of-speech are collapsed into a single vertex, whereas words that are stop-words are not. So here the text graphs should be seen as having two types of nodes corresponding to (1) candidate keyword parts for the document, and (2) stop words. The point of using these two separate types of graphs is two-fold : (1) to test if we can eliminate a pre-processing step using by using different measures, and (2) to examine the behaviour of the different centrality measures on the denser (reduced) and sparser (full) graphs.",
"cite_spans": [
{
"start": 341,
"end": 360,
"text": "(Liu et al., 2009a)",
"ref_id": "BIBREF8"
},
{
"start": 440,
"end": 459,
"text": "(Liu et al., 2009b;",
"ref_id": "BIBREF10"
},
{
"start": 460,
"end": 483,
"text": "Mihalcea & Tarau, 2004;",
"ref_id": "BIBREF11"
},
{
"start": 484,
"end": 503,
"text": "Litvak & Last, 2008",
"ref_id": "BIBREF6"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction and Previous Work",
"sec_num": "1"
},
{
"text": "For Example (1), abstract 1939 from the Test File in the Inspec corpus, first used as an example in (Mihalcea & Tarau, 2004), we give the generated reduced adjacency text graph and full adjacency text graph in Figures 1 and 2, respectively. (1)",
"cite_spans": [],
"ref_spans": [
{
"start": 210,
"end": 240,
"text": "Figures 1 and 2, respectively.",
"ref_id": null
}
],
"eq_spans": [],
"section": "Introduction and Previous Work",
"sec_num": "1"
},
{
"text": "Compatibility of systems of linear constraints over the set of natural numbers. Criteria of compatibility of a system of linear Diophantine equations, strict inequations, and nonstrict inequations are considered. Upper bounds for components of a minimal set of solutions and algorithms of construction of minimal generating sets of solutions for all types of systems are given. These criteria and the corresponding algorithms for constructing a minimal supporting set of solutions can be used in solving all the considered types systems and systems of mixed types. In this study and to maintain the unsupervised characterisation of the study, we use the MySQL stop-word list. 1",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction and Previous Work",
"sec_num": "1"
},
{
"text": "We hypothesise that keywords are the most \"central\" word vertices in a document's text graph, under some definition of centrality, which can vaguely be understood to be a summary of a node's involvement in or contribution to the cohesiveness 1. http://dev.mysql.com/doc/refman/5.5/en/fulltext-stopwords.html of the network (Borgatti & Everett, 2006) . We now describe the deterministic centrality measures that we experimented with for the keyword extraction task presented in this paper.",
"cite_spans": [
{
"start": 323,
"end": 349,
"text": "(Borgatti & Everett, 2006)",
"ref_id": "BIBREF1"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Centrality",
"sec_num": "3"
},
{
"text": "Degree-like centrality measures. The degree centrality of a vertex v in a graph G, C D (v) is simply its degree deg(v). Within the context of text graphs, this is a measure of how much of a first-hand support a text vertex (concept) is for other text vertices (concepts). Previous to the work presented in this paper, systems based on this measure were state-of-the-art (Litvak et al., 2013; Rose et al., 2010) .",
"cite_spans": [
{
"start": 370,
"end": 391,
"text": "(Litvak et al., 2013;",
"ref_id": "BIBREF7"
},
{
"start": 392,
"end": 410,
"text": "Rose et al., 2010)",
"ref_id": "BIBREF13"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Centrality",
"sec_num": "3"
},
{
"text": "Eigenvector centrality is generally considered to be a measure of the \"influence\" of a node in a graph : the more central a node is, the more central its neighbours are and so forth. In other word, vertices are important, because they have first-hand access to many other important vertices. In the context of our concept graph approximated by the text graph, the more support a concept is provided, the more total conceptual support is offered to further concepts supported by it. The output of the PageRank algorithm is meant to be a randomised variant of this measure for directed graphs (Page et al., 1999) . In fact, this ranking is also theoretically the output of the HITS algorithm (for directed graphs) upon convergence (provided all eigenvalues are distinct) (Kleinberg, 1999) .",
"cite_spans": [
{
"start": 591,
"end": 610,
"text": "(Page et al., 1999)",
"ref_id": "BIBREF12"
},
{
"start": 769,
"end": 786,
"text": "(Kleinberg, 1999)",
"ref_id": "BIBREF5"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Centrality",
"sec_num": "3"
},
{
"text": "To calculate the eigenvector centrality of a node",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Centrality",
"sec_num": "3"
},
{
"text": "v i \u2208 V (G), C EI (v i )",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Centrality",
"sec_num": "3"
},
{
"text": ", one finds the principal eigenvector of the adjacency matrix for the graph. The ith entry in this vector is C EI (v i ). From this definition, it is clear that the measure cannot be used on disconnected graphs. To surmount this difficulty, we use the PageRank \"teleportation trick\", transforming the input graph into a complete graph, simply incrementing the weight of all possible edges by 1.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Centrality",
"sec_num": "3"
},
{
"text": "Closeness centrality measures. Closeness centrality measures account for the distance of a node to all others. For computational efficiency, they consider the set of all shortest distances of a vertex x to all other nodes :",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Centrality",
"sec_num": "3"
},
{
"text": "{d(x, v) : v \u2208 V (G)}.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Centrality",
"sec_num": "3"
},
{
"text": "These measures are the least robust of all measures used in the following sense : for a disconnected graph, this sum is infinite for all vertices ; but we use the suggestion of (Dangalchev, 2006) for disconnected graphs, taking the limit in infinite calculations, so the distance between disconnected nodes is infinite and the reciprocal of this is just zero.",
"cite_spans": [
{
"start": 177,
"end": 195,
"text": "(Dangalchev, 2006)",
"ref_id": "BIBREF3"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Centrality",
"sec_num": "3"
},
{
"text": "The closeness centrality x,v) . So, the longer the distance to other word vertices (concepts), the less central the word vertex (concept) can considered. However, with this definition of closeness centrality, one cannot differentiate words that are close and far to equal numbers of nodes from those nodes that are generally close-ish to all other nodes. This is the motivation behind the eccentricity measure C ECC (x) of a vertex, which is defined as C ECC (x) := 1 max v\u2208V (G) d (x,v) .",
"cite_spans": [
{
"start": 25,
"end": 29,
"text": "x,v)",
"ref_id": null
},
{
"start": 482,
"end": 487,
"text": "(x,v)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Centrality",
"sec_num": "3"
},
{
"text": "C C (x) of a vertex x is defined as C C (x) := 1 v\u2208V (G) d(",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Centrality",
"sec_num": "3"
},
{
"text": "Betweenness centrality measures. The betweenness centrality of a vertex quantifies how often a node acts as a bridge along the shortest path between two other nodes. In the context of our text graph, the betweenness centrality can be seen as a measure of how the presentation of a scientific subject must employ a given word (concept) as support when moving the discussion between two different concepts. We consider three different betweenness centrality measures.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Centrality",
"sec_num": "3"
},
{
"text": "The (normalised) betweenness centrality C B (x) for vertex x is defined as C B (x) :",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Centrality",
"sec_num": "3"
},
{
"text": "= s\u2208V (G) t\u2208V (G) \u03c3st(x)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Centrality",
"sec_num": "3"
},
{
"text": "\u03c3st , where \u03c3 st is the number of shortest paths between nodes s and t. C B (x) gives more weight to pairs of vertices at a larger distance from each other. If one wishes to consider all shortest paths to contribute the same weight, one approach is to normalise by the shortest distance between s and t, which yields length-scaled betweenness centrality, C LSB (x) :",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Centrality",
"sec_num": "3"
},
{
"text": "= s\u2208V (G) t\u2208V (G) \u03c3st(x) d(s,t)\u03c3st .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Centrality",
"sec_num": "3"
},
{
"text": "Finally, the distance-weighted fragmentation C DW F (x) of vertex x measures the fragmentation of a graph if we took x out of it and is defined as",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Centrality",
"sec_num": "3"
},
{
"text": "C DW F (x) := C DW F (G \u2212 x) \u2212 C DW F (G), where C DW F (G) := 1 \u2212 2 i =j 1 d(i,j) n(n\u22121)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Centrality",
"sec_num": "3"
},
{
"text": ". Note that G\u2212x (the graph obtained from G by removing vertex x and any edges incident to x) should be more fragmented than G. (We also shift all scores, so that they are positive.)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Centrality",
"sec_num": "3"
},
{
"text": "Ranked keywords. In Table 1 , we give the top ranked seven words output by the degree centrality, eigenvector centrality, closeness centrality and betweenness centrality for Example (1). ",
"cite_spans": [],
"ref_spans": [
{
"start": 20,
"end": 27,
"text": "Table 1",
"ref_id": "TABREF1"
}
],
"eq_spans": [],
"section": "Centrality",
"sec_num": "3"
},
{
"text": "C D (v",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Centrality",
"sec_num": "3"
},
{
"text": "We carry out similar post-processing to (Mihalcea & Tarau, 2004) . That is, sequences of adjacent keywords from the text are possibly collapsed into a multi-word keyword, depending on their scores. We score a multi-unit keyword by the average (ave) score of words they are composed with. This yields a candidate list where there may be unit overlaps in keywords. We therefore test an extra post-processing step which keeps only the keyword with the highest score among two overlapping keywords (this corresponds to ave-excl in Table 4 ). Ties are broken with a preference for longer keywords ; moreover, the proposed keywords must not start or end with a stop-word, and must be grounded in a noun (i.e., the rightmost word of a multi-word keyword must be a noun). Keywords consisting of at most three words are considered.",
"cite_spans": [
{
"start": 40,
"end": 64,
"text": "(Mihalcea & Tarau, 2004)",
"ref_id": "BIBREF11"
}
],
"ref_spans": [
{
"start": 527,
"end": 534,
"text": "Table 4",
"ref_id": null
}
],
"eq_spans": [],
"section": "Evaluation",
"sec_num": "4"
},
{
"text": "Results are evaluated using average standard Normalised Discounted Cumulative Gain (NDCG) which is mathematically proven to distinguish between ranking systems that are sufficiently different from each other (Wang et al., 2013) . Recall that NDCG is carried out on the entire ranked list and not on some top-n items ; System A is considered superior to System B if the positives (correct keywords) are generally ranked higher by System A than by System B according to the NDCG metric.",
"cite_spans": [
{
"start": 208,
"end": 227,
"text": "(Wang et al., 2013)",
"ref_id": "BIBREF17"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Evaluation",
"sec_num": "4"
},
{
"text": "We see that there is the best performing system uses C LSB (with (ave-excl)-full). Moreover, we observe that the postprocessing step which does not allow overlapping keywords in the candidate list performs better across all measures ; this is probably because \"close duplicates\" that would otherwise dilute the higher ranking positions are excluded.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Evaluation",
"sec_num": "4"
},
{
"text": "We also consider that the use of the set-based evaluation metrics of precision, recall, and f-score are misleading for rankbased systems, but may be informative as a means of error analysis when considering best parameter performance : poor system performance can possibly be explained by a system reaching its optimal f-score too early (for example, n = 1), or too late (for example, n = 50) in the ranked list. The best parameter f-scored system is C B -(ave)-full occurs at n = 9, with only half of the list being true positives.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Evaluation",
"sec_num": "4"
},
{
"text": "The eigenvector centrality measure is equivalent to PageRank and we test the degree centrality measure. These are the two methods that have previously been tested for non-contextual SDKE, in (Mihalcea & Tarau, 2004) and (Rose et al., 2010) respectively. The length-scaled betweenness centrality measure outscores both of these measures in NDCG.",
"cite_spans": [
{
"start": 191,
"end": 215,
"text": "(Mihalcea & Tarau, 2004)",
"ref_id": "BIBREF11"
},
{
"start": 220,
"end": 239,
"text": "(Rose et al., 2010)",
"ref_id": "BIBREF13"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Evaluation",
"sec_num": "4"
},
{
"text": "In terms of graph structure, we see that in general the full graph is preferred, which attests to the robustness of the measures and their preference for as much information as possible in the graph. ",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Evaluation",
"sec_num": "4"
}
],
"back_matter": [],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "showing that firm understanding of the \"flavour\" of centrality we are trying to predict is essential in keyword extraction tasks. Some open questions remain. For instance, how robust are these measures on large document graphs ? Also, with larger graphs, time and space becomes an issue : how can these measures be efficiently",
"authors": [],
"year": null,
"venue": "scientific text",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "presented a study on the essence of keywords in scientific text, showing that firm understanding of the \"flavour\" of centrality we are trying to predict is essential in keyword extraction tasks. Some open questions remain. For instance, how robust are these measures on large document graphs ? Also, with larger graphs, time and space becomes an issue : how can these measures be efficiently computed in general ? Large documents pose the next challenge.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "A graph-theoretic perspective on centrality",
"authors": [
{
"first": "",
"middle": [
"P"
],
"last": "Borgatti S",
"suffix": ""
},
{
"first": "",
"middle": [
"G"
],
"last": "Everett M",
"suffix": ""
}
],
"year": 2006,
"venue": "Social Networks",
"volume": "28",
"issue": "4",
"pages": "466--484",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "BORGATTI S. P. & EVERETT M. G. (2006). A graph-theoretic perspective on centrality. Social Networks, 28(4), 466 - 484.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "The anatomy of a large-scale hypertextual web search engine",
"authors": [
{
"first": "",
"middle": [],
"last": "Brin S",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Page L",
"suffix": ""
}
],
"year": 1998,
"venue": "Seventh International World-Wide Web Conference",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "BRIN S. & PAGE L. (1998). The anatomy of a large-scale hypertextual web search engine. In Seventh International World-Wide Web Conference (WWW 1998).",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Residual closeness in networks",
"authors": [
{
"first": "C",
"middle": [],
"last": "Dangalchev",
"suffix": ""
}
],
"year": 2006,
"venue": "Physica A : Statistical Mechanics and its Applications",
"volume": "365",
"issue": "2",
"pages": "556--564",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "DANGALCHEV C. (2006). Residual closeness in networks. Physica A : Statistical Mechanics and its Applications, 365(2), 556-564.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Improved automatic keyword extraction given more linguistic knowledge",
"authors": [
{
"first": "",
"middle": [],
"last": "Hulth A",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of the 2003 conference on Empirical methods in natural language processing, EMNLP '03",
"volume": "",
"issue": "",
"pages": "216--223",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "HULTH A. (2003). Improved automatic keyword extraction given more linguistic knowledge. In Proceedings of the 2003 conference on Empirical methods in natural language processing, EMNLP '03, p. 216-223, Stroudsburg, PA, USA : Association for Computational Linguistics.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Authoritative sources in a hyperlinked environment",
"authors": [
{
"first": "J",
"middle": [
"M"
],
"last": "Kleinberg",
"suffix": ""
}
],
"year": 1999,
"venue": "J. ACM",
"volume": "46",
"issue": "5",
"pages": "604--632",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "KLEINBERG J. M. (1999). Authoritative sources in a hyperlinked environment. J. ACM, 46(5), 604-632.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Graph-based keyword extraction for single-document summarization",
"authors": [
{
"first": "",
"middle": [],
"last": "Litvak M. & Last M",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of the Workshop on Multi-source Multilingual Information Extraction and Summarization, MMIES '08",
"volume": "",
"issue": "",
"pages": "17--24",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "LITVAK M. & LAST M. (2008). Graph-based keyword extraction for single-document summarization. In Procee- dings of the Workshop on Multi-source Multilingual Information Extraction and Summarization, MMIES '08, p. 17-24, Stroudsburg, PA, USA : Association for Computational Linguistics.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Degext : a language-independent keyphrase extractor",
"authors": [
{
"first": "M",
"middle": [],
"last": "Litvak",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Last M. & Kandel A",
"suffix": ""
}
],
"year": 2013,
"venue": "J. Ambient Intelligence and Humanized Computing",
"volume": "4",
"issue": "3",
"pages": "377--387",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "LITVAK M., LAST M. & KANDEL A. (2013). Degext : a language-independent keyphrase extractor. J. Ambient Intelligence and Humanized Computing, 4(3), 377-387.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Unsupervised approaches for automatic keyword extraction using meeting transcripts",
"authors": [
{
"first": "",
"middle": [],
"last": "Liu F",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Pennell D",
"suffix": ""
},
{
"first": "",
"middle": [
"&"
],
"last": "Liu F",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Liu Y",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of Human Language Technologies : The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL '09",
"volume": "",
"issue": "",
"pages": "620--628",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "LIU F., PENNELL D., LIU F. & LIU Y. (2009a). Unsupervised approaches for automatic keyword extraction using meeting transcripts. In Proceedings of Human Language Technologies : The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, NAACL '09, p. 620-628, Stroudsburg, PA, USA : Association for Computational Linguistics.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Automatic keyphrase extraction via topic decomposition",
"authors": [
{
"first": "",
"middle": [],
"last": "Liu Z",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Huang W",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Zheng",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Sun M",
"suffix": ""
}
],
"year": 2010,
"venue": "Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "366--376",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "LIU Z., HUANG W., ZHENG Y. & SUN M. (2010). Automatic keyphrase extraction via topic decomposition. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP '10, p. 366-376, Stroudsburg, PA, USA : Association for Computational Linguistics.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Clustering to find exemplar terms for keyphrase extraction",
"authors": [
{
"first": "",
"middle": [],
"last": "Liu Z",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Li P",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Zheng",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Sun M",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing",
"volume": "1",
"issue": "",
"pages": "257--266",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "LIU Z., LI P., ZHENG Y. & SUN M. (2009b). Clustering to find exemplar terms for keyphrase extraction. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing : Volume 1 -Volume 1, EMNLP '09, p. 257-266, Stroudsburg, PA, USA : Association for Computational Linguistics.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Textrank : Bringing order into text",
"authors": [
{
"first": "",
"middle": [],
"last": "Mihalcea R. & Tarau P",
"suffix": ""
}
],
"year": 2004,
"venue": "Proceedings of EMNLP",
"volume": "",
"issue": "",
"pages": "404--411",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "MIHALCEA R. & TARAU P. (2004). Textrank : Bringing order into text. In Proceedings of EMNLP, p. 404-411.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "The PageRank Citation Ranking : Bringing Order to the Web",
"authors": [
{
"first": "Page",
"middle": [
"L"
],
"last": "Brin",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Motwani R. & Winograd T",
"suffix": ""
}
],
"year": 1999,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "PAGE L., BRIN S., MOTWANI R. & WINOGRAD T. (1999). The PageRank Citation Ranking : Bringing Order to the Web. Technical Report 1999-66, Stanford InfoLab. Previous number = SIDL-WP-1999-0120.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Automatic Keyword Extraction from Individual Documents",
"authors": [
{
"first": "Rose",
"middle": [
"S"
],
"last": "Engel D",
"suffix": ""
},
{
"first": "",
"middle": [
"&"
],
"last": "Cramer N",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Cowley W",
"suffix": ""
}
],
"year": 2010,
"venue": "Text Mining. Applications and Theory",
"volume": "",
"issue": "",
"pages": "1--20",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "ROSE S., ENGEL D., CRAMER N. & COWLEY W. (2010). Automatic Keyword Extraction from Individual Documents, In Text Mining. Applications and Theory, p. 1-20. John Wiley and Sons, Ltd.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Feature-rich part-of-speech tagging with a cyclic dependency network",
"authors": [
{
"first": "",
"middle": [],
"last": "Toutanova K",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Klein D",
"suffix": ""
},
{
"first": "",
"middle": [
"D"
],
"last": "Manning C",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Singer Y",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology",
"volume": "1",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "TOUTANOVA K., KLEIN D., MANNING C. D. & SINGER Y. (2003). Feature-rich part-of-speech tagging with a cyclic dependency network. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Com- putational Linguistics on Human Language Technology -Volume 1, NAACL '03, Stroudsburg, PA, USA : Association for Computational Linguistics.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Single document keyphrase extraction using neighborhood knowledge",
"authors": [
{
"first": "Wan",
"middle": [
"X"
],
"last": "Xiao J",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of the 23rd national conference on Artificial intelligence",
"volume": "2",
"issue": "",
"pages": "855--860",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "WAN X. & XIAO J. (2008). Single document keyphrase extraction using neighborhood knowledge. In Proceedings of the 23rd national conference on Artificial intelligence -Volume 2, AAAI'08, p. 855-860 : AAAI Press.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Towards an iterative reinforcement approach for simultaneous document summarization and keyword extraction",
"authors": [
{
"first": "Wan",
"middle": [
"X"
],
"last": "",
"suffix": ""
},
{
"first": "Yang",
"middle": [
"J"
],
"last": "Xiao J",
"suffix": ""
}
],
"year": 2007,
"venue": "ACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "WAN X., YANG J. & XIAO J. (2007). Towards an iterative reinforcement approach for simultaneous document summa- rization and keyword extraction. In ACL.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "A theoretical analysis of ndcg type ranking measures",
"authors": [
{
"first": "Wang",
"middle": [
"Y"
],
"last": "Wang L",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "D",
"middle": [
"&"
],
"last": "He",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Liu T.-Y",
"suffix": ""
}
],
"year": 2013,
"venue": "Proceedings of COLT",
"volume": "",
"issue": "",
"pages": "25--54",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "WANG Y., WANG L., LI Y., HE D. & LIU T.-Y. (2013). A theoretical analysis of ndcg type ranking measures. In Proceedings of COLT, p. 25-54.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Topical keyphrase extraction from twitter",
"authors": [
{
"first": "",
"middle": [
"X"
],
"last": "Zhao W",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Jiang",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "He",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Song",
"suffix": ""
},
{
"first": "P",
"middle": [],
"last": "Achananuparp",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Lim E.-P",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Li X",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics : Human Language Technologies",
"volume": "1",
"issue": "",
"pages": "379--388",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "ZHAO W. X., JIANG J., HE J., SONG Y., ACHANANUPARP P., LIM E.-P. & LI X. (2011). Topical keyphrase extrac- tion from twitter. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics : Human Language Technologies -Volume 1, HLT '11, p. 379-388, Stroudsburg, PA, USA : Association for Computational Lin- guistics.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"uris": null,
"text": "Reduced (left) and full (right) adjacency graph for Example (1).",
"type_str": "figure",
"num": null
},
"TABREF1": {
"content": "<table/>",
"html": null,
"text": "Top seven words from ranked lists for Example (1) across a selection of centrality measures, for the full text adjacency graph.",
"type_str": "table",
"num": null
},
"TABREF3": {
"content": "<table/>",
"html": null,
"text": "NDCG and best parameter (n) precision, recall and f-score for all centrality measures tested.",
"type_str": "table",
"num": null
}
}
}
} |