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README.md

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 ---
 license: cc-by-nc-4.0
+task_categories:
+- summarization
+language:
+- ko
+tags:
+- video
+- summarization
+- highlight-detection
+- multimodal
+- most-replayed
+- youtube
 ---
+
+## Dataset Summary
+
+KoSum is a Korean YouTube benchmark for video summarization and highlight detection. It contains 700 recent Korean YouTube videos uploaded between 2024 and 2026, spanning 14 fine-grained content categories. KoSum provides per-second visual, audio, and text features aligned with YouTube Most-Replayed importance scores.
+
+The dataset is designed for multimodal video summarization, highlight detection, and analysis of viewer-engagement-driven editing patterns.
+
+* **Project Page:** [iontail.github.io/kosum](https://iontail.github.io/kosum/)
+
+## Dataset Structure
+
+The dataset contains metadata, multimodal features, ground-truth annotations, and standardized train/validation/test splits.
+
+### 1. Metadata (`kosum_metadata.csv`)
+
+Contains metadata for all 700 videos.
+
+* **video_id**: A short internal metadata ID.
+* **youtube_id**: The original YouTube video ID. This is the key used in all HDF5 files and `kosum_split.json`.
+* **title**: YouTube video title.
+* **duration**: Video duration in seconds.
+* **views**: YouTube view count.
+* **category1**: Broad content category.
+* **category2**: Fine-grained content category.
+* **likes**: YouTube like count.
+* **comments**: YouTube comment count.
+* **category_id**: YouTube category ID.
+* **category**: YouTube category name.
+* **cap_lan**: Caption language label.
+
+KoSum has 14 fine-grained categories with 50 videos per category: `action`, `animation`, `baseball`, `basketball`, `cooking`, `football`, `job`, `knowledge`, `lecture`, `review`, `romance`, `sketch`, `talk`, and `vlog`.
+
+### 2. Multimodal Features (`.h5` files)
+
+Each feature file is an HDF5 file with 700 top-level datasets keyed by `youtube_id`. Each dataset has shape `(T, 768)`, where `T` is the per-second sequence length for the video.
+
+* **kosum_feat_visual_clip.h5**: Visual features extracted with CLIP (`openai/clip-vit-large-patch14`).
+* **kosum_feat_audio_ast.h5**: Audio features extracted with Audio Spectrogram Transformer (`MIT/ast-finetuned-audioset-10-10-0.4593`).
+* **kosum_feat_text_roberta.h5**: Text features extracted from subtitles with XLM-RoBERTa (`FacebookAI/xlm-roberta-base`).
+
+All feature datasets are stored as `float32`. The sequence lengths range from 383 to 1800 seconds.
+
+Example HDF5 layout:
+
+```text
+kosum_feat_visual_clip.h5
+`-- {youtube_id}: float32[T, 768]
+
+kosum_feat_audio_ast.h5
+`-- {youtube_id}: float32[T, 768]
+
+kosum_feat_text_roberta.h5
+`-- {youtube_id}: float32[T, 768]
+```
+
+> **File Size & Downloading:** Each feature file is approximately 2GB. Download time may vary depending on network conditions.
+
+### 3. Ground Truth (`kosum_gt.h5`)
+
+An HDF5 file containing ground-truth annotations for all 700 videos. Each `youtube_id` maps to an HDF5 group with the following keys:
+
+* **change_points**: Shot boundaries generated by KTS. Shape is `(num_shots, 2)`, stored as `int32`.
+* **gt_score**: Per-second Most-Replayed importance scores. Shape is `(T,)`, stored as `float32`.
+* **gt_summary**: Binary summary labels. Shape is `(T,)`, stored as `int8`.
+
+Each group also stores `youtube_id`, `score_key`, `feature_key`, `category1`, and `category2` as HDF5 attributes.
+
+Example HDF5 layout:
+
+```text
+kosum_gt.h5
+`-- {youtube_id}/
+    |-- change_points: int32[num_shots, 2]
+    |-- gt_score: float32[T]
+    `-- gt_summary: int8[T]
+```
+
+### 4. Dataset Splits (`kosum_split.json`)
+
+Contains standardized splits using `youtube_id` keys.
+
+* **train_keys**: 560 training videos.
+* **val_keys**: 70 validation videos.
+* **test_keys**: 70 test videos.
+
+The split follows an 80/10/10 ratio and keeps each `category2` balanced across train, validation, and test sets.
+
+## Loading Example
+
+```python
+import h5py
+
+video_id = "1_1aaQtxemQ"
+
+with h5py.File("kosum_feat_visual_clip.h5", "r") as visual_h5:
+    visual_feat = visual_h5[video_id][:]
+
+with h5py.File("kosum_gt.h5", "r") as gt_h5:
+    gt_score = gt_h5[video_id]["gt_score"][:]
+    gt_summary = gt_h5[video_id]["gt_summary"][:]
+    change_points = gt_h5[video_id]["change_points"][:]
+```
+
+## Citation
+
+If you use KoSum in your research, please cite:
+
+```bibtex
+@misc{lee2026kosum,
+  title  = {KoSum: Beyond Detection: Most-Replayed Driven Multimodal Analysis of Korean YouTube Videos for Highlight Editing Guidance},
+  author = {Lee, Chanhee and Jang, Jinho and Ha, Sungjun and Jung, Jinwoong},
+  year   = {2026},
+  note   = {2026 Spring DSC3028 Capstone Project Technical Report, Sungkyunkwan University}
+}
+```

kosum_feat_audio_ast.h5

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+size 2190520576

kosum_feat_text_roberta.h5

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+size 2190520576

kosum_feat_visual_clip.h5

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kosum_gt.h5

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kosum_split.json

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+    "dnVpwwXLr5c",
+    "hu6Ml5Yff5Q",
+    "s8sCDxZJQ-A",
+    "tsJ512CUeuM",
+    "qN-A9neQUFA",
+    "qufvxE1NG2Y",
+    "o9ic9eJiskE",
+    "WefJtbkpHA8",
+    "4kfaoJlUpr8",
+    "CMpQHhVJu5Q",
+    "NVdhbF2geE8",
+    "WRrpHU-DaYs",
+    "bdlTafRkKdQ",
+    "TpQ8ZQKb7hw",
+    "S3SZ6nP7GmM",
+    "QPrx6SWhl4Q",
+    "844VbQx7nWk",
+    "OUNOSgvcvXY",
+    "l35ok1jy3_0",
+    "AFjcmeOmyGU",
+    "H2B6C7rCqtA",
+    "yd4adviJaFQ",
+    "XucfH5effB8",
+    "i6ngM6950aM",
+    "PBk7AiAVRbY",
+    "1UBz3oa8dGQ",
+    "7hFr51eO5tU",
+    "94xW-bw6IIE",
+    "42LFCziEkms",
+    "JLLlKmbn-uU",
+    "DOuXlaCRPDQ",
+    "wzj6iUHPnPQ",
+    "Hz54KyAWU3E",
+    "hoWMXI4Iui8",
+    "-jYBBd0QBDk",
+    "3Ba8OhXQe7E",
+    "xRzvjgod_U8",
+    "RprSKiD1Bd8",
+    "dCbgYN2jM1k",
+    "o-aZhMBQkTk",
+    "9_mz0TZ0LeM",
+    "E569bEa7AxQ",
+    "FyUVGHb9xwg",
+    "5FB-CMNTQMU",
+    "lPdcnWAuJQU",
+    "zbHGuztgYYQ",
+    "F5SVpE9xuUE",
+    "Rv38Y54GoZc",
+    "iFiibbDwUx8",
+    "MYjibzvUMM0",
+    "O_QDLdAY5nI",
+    "h2sSFb4iO5w",
+    "gaZV9G2ZZDc",
+    "yRSZfzO-5w8",
+    "Ytx8SWfz76A",
+    "ODg5fVQavRk",
+    "-3m8Ji0Fpws",
+    "9FVMFMGCxP4",
+    "rrmKsDkhqvo",
+    "i8ui4niEFSI",
+    "-IgIv5yQ-Ck",
+    "Aj1lWvY6mxk",
+    "UMTZlPRnq8g",
+    "05Q3svWmri8",
+    "LLcaGyijrFQ",
+    "HjGYEpoW-hU",
+    "udufvSb8zKA",
+    "uYEcUsR1-9A",
+    "7rnZ7Rv47EA",
+    "71LL5w8G3KM",
+    "ctICwngnr0g",
+    "K_BtEvBzF1U",
+    "ewH5DOWrfEU",
+    "k3vZpksTczI",
+    "40RYokzQzZ0",
+    "E7DBvMq9VOA",
+    "RDlEhNqFPUU",
+    "cIsajQ03njQ",
+    "JtggIDMjCw4",
+    "x8JOdj82A_o",
+    "cYRBz0-Jo70",
+    "h0KR63LcBfc",
+    "rd6WFX6UEUk",
+    "FcKPArjWgHM",
+    "0oU8TAsHjZY",
+    "iC9MqPlr658",
+    "8o0KmZL1mR0",
+    "DoMlmYE7SpU",
+    "CwBuIHCTl3g",
+    "FD2GudlTXiE",
+    "sgL6OPGjHgo",
+    "1BvkWC_ZT4Y",
+    "XR6OQ2NSgFA",
+    "MJM1V4hmHGY",
+    "rrQIVezQNhE",
+    "3fjGJ29HK64",
+    "8sE4hCDc44s",
+    "gfKHiZK1Bp8",
+    "yEg1jqTXKvY",
+    "Yhpm8u-oB3s",
+    "ZCSSR-zctI0",
+    "H5UrlNRSzYc",
+    "ia_MvwcEszo",
+    "WCl6pxJL6kA",
+    "zSIpGdP2iHA",
+    "bASosYoNK4A",
+    "NtHWy8_GLto",
+    "k1dy5juTaHY",
+    "OV5B8V-7Ilg",
+    "A9OG4aVjOg4",
+    "hq2VPcPbBWY",
+    "IsRoJ7btK50",
+    "a232LdtysFs",
+    "-90P9-ezqKM",
+    "2Y_1AqcL2I0",
+    "R24v-Dz-I-U",
+    "rGykh8bEOH8",
+    "XK2-gXA-WIg",
+    "IqBTG2IyaAE",
+    "OGwxiMSeiZA",
+    "1qAMGlIWTOA",
+    "UPkyaO35RdM",
+    "VonXaUKIl0w",
+    "T_zLzMaGT6o",
+    "8kvg97sWr1U",
+    "sTTJx70Fl-U",
+    "jSdrrmlkpqA",
+    "RE0k_4Ip_ns",
+    "EUyDpUDjjus",
+    "cZq0pmn9690",
+    "03Uo9kziUDg",
+    "sxGYNFyOFU0",
+    "gnbY14dbHhY",
+    "WediR9K_yFw",
+    "FHzo7isws6U",
+    "DA6OeVq2DOM",
+    "t749yVzlsRI",
+    "VjBC8Nz3_wY",
+    "k1rrDJQPU2Y",
+    "8AVCbKEwp6E",
+    "0VCVlonWxrQ"
+  ],
+  "val_keys": [
+    "Heh80xExdq0",
+    "YnXAwK2R82s",
+    "70QdcUtarrc",
+    "hc8OiSOdpiI",
+    "TFGvim4H7hY",
+    "Qh3nVLl8U2s",
+    "YxQKcSHfz24",
+    "BT0RkEEhHcY",
+    "YuAMHo3ppz8",
+    "RdSFYzS7PiA",
+    "bcqW5wKKLDw",
+    "mpy6S8Hnku8",
+    "wfFuPhcvlOE",
+    "SvOAijla0IM",
+    "J1bIwm8KLXE",
+    "PfG0-qrwngg",
+    "IGxXGT3Mte8",
+    "5lwJvwkvsWg",
+    "WgzHh2XjpS8",
+    "Wz4W-Kvr7PY",
+    "ojp87mTFpkg",
+    "KjwQbYF-j_Y",
+    "DHoyLOU3r4A",
+    "5W7xcvYk3kg",
+    "ARfGRsrxy3M",
+    "CT4VC7STo5o",
+    "4etTGx7RQvg",
+    "C8XUt44Z69A",
+    "NsuNCvjEYdE",
+    "Mglpmg0biUM",
+    "t0wcdWkb2i0",
+    "2lQXIsJ1JcY",
+    "FRbO1Vjnjos",
+    "JftQba0hGA0",
+    "WMhzAHt2r7g",
+    "AVY8M2QX3Eg",
+    "w4uALrp9lHs",
+    "PtGsKbpELxA",
+    "5SMRW0u_2u0",
+    "54hnZ1PxszY",
+    "h2TX_6EUItA",
+    "x5SoIg0WuKU",
+    "ixixjQmExsg",
+    "KWwu0yQf5jY",
+    "88UmrqlNYEY",
+    "nDlhDDHW_8s",
+    "B_wnFy-BEzg",
+    "j42Kc7Kx-eQ",
+    "ofuUWYmN0gI",
+    "isZhRtOC02k",
+    "DN2W-n1C3cQ",
+    "3rpIe25w90E",
+    "KBGRJU3cKkw",
+    "yqfsAceb3r0",
+    "GGOySnLKmUk",
+    "mjgKn_imKHc",
+    "gUZK-twy-QM",
+    "WrmHLaT7gyw",
+    "vY8YlBEdNCc",
+    "bodJMoaKBDU",
+    "-PTkq0Y-G-U",
+    "wp-hl7slupw",
+    "XYMb3zgL_70",
+    "jQNuVeErFmM",
+    "jtoUOnuQfu4",
+    "8c4pUKAzI24",
+    "0CnEduDB6VI",
+    "s2_yCzEMRmc",
+    "WJ8mCAvmnaQ",
+    "krc6ir_IV4Y"
+  ],
+  "test_keys": [
+    "lJwp6ORCxqo",
+    "H6ev3F4xLfY",
+    "BJOEa0LjLYw",
+    "NsjdK2fSSyQ",
+    "5N31tgrNTfc",
+    "SZhx6ARcOW4",
+    "AY_aAWrB6vM",
+    "HTW_c72R24o",
+    "7BeeQipYT58",
+    "0RRp6NIOeQU",
+    "1uShXVvn59A",
+    "ZSIbfc-edvw",
+    "J10akkl1OsI",
+    "Dpb3UvImUoo",
+    "lCfqTTZ_Auo",
+    "0bAsnkduoC8",
+    "cyBc72zeT2A",
+    "xOJSAAghQXs",
+    "ZOKTpMQ1pzY",
+    "YTLyTZZsA10",
+    "sii46WTLReA",
+    "6ILihPuFHDI",
+    "cc4esRtmR64",
+    "I6qULxdtP-g",
+    "gwWigrlzSZw",
+    "RoYUmoP7m64",
+    "N2uYZe1GrJk",
+    "pZHMCZFrrds",
+    "3bSKl2v2jw8",
+    "rz30tf6uRlY",
+    "Y4WJe9Rbsw8",
+    "KRL4KRS_s_M",
+    "4QDeHQy-bhU",
+    "N-QlTzNu5hg",
+    "07UfrjYN2fU",
+    "BvOU0FmTqh8",
+    "XfeR1iODiwA",
+    "8Sc4gKYD9A8",
+    "TNEPJJQbi2E",
+    "iJPXMPgzMcQ",
+    "XrU52fG7jgI",
+    "C5QlX4rWqgw",
+    "SGolR7GZQkI",
+    "_sf2MsRKvGk",
+    "1D8wWBvVprQ",
+    "fJXNllTVs-U",
+    "f5dBWy-F0YY",
+    "iYKtucB0MjA",
+    "GXrIUaLw3T8",
+    "-rWQNF231gs",
+    "1qnOtyawWmg",
+    "jvBBcKfQ03c",
+    "3vaFFVJGCw4",
+    "P-y7ecqFmxA",
+    "PXKTcDBn13g",
+    "MCbJiC7MVRA",
+    "BdF29IC2o30",
+    "je84ZH50djQ",
+    "6FdidevImm8",
+    "w1_BvtZTmYk",
+    "EYjqmcEtDbY",
+    "_V1n1MXF-Wg",
+    "dqmNtJr8O_E",
+    "v5rilLK1ABE",
+    "snEQhuvaX78",
+    "HaYNqWBaanI",
+    "FtQnL4GBsoI",
+    "YT2Tv2h6vgc",
+    "p8ITrh6EOIU",
+    "qqU88B0eD0A"
+  ]
+}