| --- |
| 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"][:] |
| ``` |
| |
| ## Data Issues |
| |
| If you find any data issue, such as missing keys, broken files, or metadata/annotation errors, please let us know through the Community. |
| |
| ## 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} |
| } |
| ``` |
| |
