README.md

---
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}
}
```