README.md

---
language:
- en
license: cc-by-nc-sa-4.0
size_categories:
- 10K<n<100K
task_categories:
- token-classification
- automatic-speech-recognition
pretty_name: YTSeg
tags:
- text segmentation
- smart chaptering
- segmentation
- youtube
- asr
configs:
- config_name: audio
  data_files:
  - split: train
    path: audio/train-*
  - split: validation
    path: audio/validation-*
  - split: test
    path: audio/test-*
- config_name: text
  data_files:
  - split: train
    path: text/train-*
  - split: validation
    path: text/validation-*
  - split: test
    path: text/test-*
- config_name: titles
  data_files:
  - split: train
    path: titles/train-*
  - split: validation
    path: titles/validation-*
  - split: test
    path: titles/test-*
dataset_info:
- config_name: audio
  features:
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: text_ref
    list: string
  - name: text_wt
    list: string
  - name: text_wl
    list: string
  - name: target_binary_ref
    dtype: string
  - name: target_binary_wt
    dtype: string
  - name: target_binary_wl
    dtype: string
  - name: target_text_ref
    dtype: string
  - name: target_text_ts_ref
    dtype: string
  - name: target_ts
    dtype: string
  - name: chapter_titles
    list: string
  - name: chapter_timestamps
    list: float64
  - name: channel_id
    dtype: string
  - name: video_id
    dtype: string
  - name: speaker_category
    dtype: string
  - name: dominant_speaker_proportion
    dtype: float64
  - name: num_speakers
    dtype: int64
  - name: duration
    dtype: float64
  splits:
  - name: train
    num_bytes: 62127154526
    num_examples: 16404
  - name: validation
    num_bytes: 5483478090
    num_examples: 1447
  - name: test
    num_bytes: 5658475811
    num_examples: 1448
  download_size: 71470669858
  dataset_size: 73269108427
- config_name: text
  features:
  - name: text_ref
    sequence: string
  - name: text_wt
    sequence: string
  - name: text_wl
    sequence: string
  - name: target_binary_ref
    dtype: string
  - name: target_binary_wt
    dtype: string
  - name: target_binary_wl
    dtype: string
  - name: target_text_ref
    dtype: string
  - name: target_text_ts_ref
    dtype: string
  - name: target_ts
    dtype: string
  - name: audio_path
    dtype: string
  - name: chapter_titles
    sequence: string
  - name: chapter_timestamps
    sequence: float64
  - name: channel_id
    dtype: string
  - name: video_id
    dtype: string
  - name: speaker_category
    dtype: string
  - name: dominant_speaker_proportion
    dtype: float64
  - name: num_speakers
    dtype: int64
  - name: duration
    dtype: float64
  splits:
  - name: train
    num_bytes: 1569140592
    num_examples: 16404
  - name: validation
    num_bytes: 141371796
    num_examples: 1447
  - name: test
    num_bytes: 142148239
    num_examples: 1448
  download_size: 1035208355
  dataset_size: 1852660627
- config_name: titles
  features:
  - name: text_section_ref
    dtype: string
  - name: text_section_prev_titles_ref
    dtype: string
  - name: target_title
    dtype: string
  - name: channel_id
    dtype: string
  - name: video_id
    dtype: string
  - name: chapter_idx
    dtype: int64
  splits:
  - name: train
    num_bytes: 614309842
    num_examples: 146907
  - name: validation
    num_bytes: 55897452
    num_examples: 13206
  - name: test
    num_bytes: 56121869
    num_examples: 13082
  download_size: 389373572
  dataset_size: 726329163
---

# From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptions

We present <span style="font-variant:small-caps; font-weight:700;">YTSeg</span>, a topically and structurally diverse benchmark for the text segmentation task based on YouTube transcriptions. The dataset comprises 19,299 videos from 393 channels, amounting to 6,533 content hours. The topics are wide-ranging, covering domains such as science, lifestyle, politics, health, economy, and technology. The videos are from various types of content formats, such as podcasts, lectures, news, corporate events \& promotional content, and, more broadly, videos from individual content creators. We refer to the **paper** ([acl](https://aclanthology.org/2024.eacl-long.25/) | [arXiv](https://arxiv.org/abs/2402.17633)) for further information. We provide both text and audio data as well as a download script for the video data.

## Data Overview

We offer three dataset subsets:
- **Text** — For text-based segmentation and chaptering approaches using transcripts.
- **Audio** — For audio-based chaptering approaches with embedded audio.
- **Titles** — For chapter title generation given segment text (relevant for two-stage approaches).

### <span style="font-variant:small-caps;">YTSeg</span> (Text)

Each video is represented as a JSON object. The fields are organized into three categories: **Transcripts**, **Target Representations**, and **Metadata**.

#### Transcripts

We provide three transcript variants for each video: the original reference transcript and two ASR-generated transcripts using Whisper models.

| Field  | Description |
|--------|-------------|
| `text_ref` | Reference transcript as a flat list of sentences. |
| `text_wt` | Whisper-tiny ASR transcript as a flat list of sentences. |
| `text_wl` | Whisper-large ASR transcript as a flat list of sentences. |

#### Target Representations

Multiple target formats are provided for different modeling approaches.

| Field  | Description |
|--------|-------------|
| `target_binary_ref` | Binary segmentation labels for reference transcript (e.g., `\|=000100000010`). |
| `target_binary_wt` | Binary segmentation labels for Whisper-tiny transcript. |
| `target_binary_wl` | Binary segmentation labels for Whisper-large transcript. |
| `target_text_ref` | Structured transcript with chapter markers (e.g., `[CSTART] Title [CEND] text...`). |
| `target_text_ts_ref` | Structured transcript with timestamped chapter markers (e.g., `[CSTART] 00:01:23 - Title [CEND] text...`). |
| `target_ts` | Timestamped chapter markers only (e.g., `[CSTART] 00:01:23 - Title [CEND]\n...`). |

#### Metadata

| Field  | Description |
|--------|-------------|
| `audio_path` | Path to the .mp3 file of the video. |
| `chapter_titles` | A list of chapter titles corresponding to each segment. |
| `chapter_timestamps` | A list of chapter start times in seconds (e.g., `[0.0, 25.0, 269.0]`). |
| `channel_id` | The YouTube channel ID which this video belongs to. |
| `video_id` | The YouTube video ID. |
| `speaker_category` | Speaker classification: `single`, `single_weak`, or `multiple`. |
| `dominant_speaker_proportion` | Proportion of speech from the dominant speaker (0.0-1.0). |
| `num_speakers` | Number of detected speakers in the video. |
| `duration` | Video duration in seconds. |

#### Partition Statistics

| Partition  | # Examples    |
|------------|--------------|
| Training   | 16,404 (85%)   |
| Validation | 1,447 (7.5%)   |
| Testing    | 1,448 (7.5%)   |
| Total      | 19,299        |

### <span style="font-variant:small-caps;">YTSeg</span> (Audio)

The audio config provides the complete dataset with embedded audio files. Each video is represented with the same fields as the text config, plus an `audio` field containing the preprocessed audio data.

#### Audio

| Field  | Description |
|--------|-------------|
| `audio` | Audio data preprocessed into .mp3 format with a standardized sample rate of 16,000 Hz and a single channel (mono). |

All other fields (transcripts, target representations, and metadata) are identical to the Text config described above.

#### Partition Statistics

| Partition  | # Examples    | Size (GB) |
|------------|--------------|-----------|
| Training   | 16,404 (85%)   | ~57.9 GB |
| Validation | 1,447 (7.5%)   | ~5.1 GB |
| Testing    | 1,448 (7.5%)   | ~5.3 GB |
| Total      | 19,299        | ~68.3 GB |

### <span style="font-variant:small-caps;">YTSeg</span> (Titles)

Each chapter of a video is represented as a JSON object with the following fields:

| Field  | Description                                    |
|--------------|------------------------------------------------|
| `text_section_ref`       | The complete chapter/section text.           |
| `text_section_prev_titles_ref`    | The complete chapter/section text with previous section titles prepended. |
| `target_title`     | The target chapter title.           |
| `channel_id` | The YouTube channel ID which this chapter's video belongs to.          |
| `video_id`   | The YouTube video ID which this chapter belongs to.            |
| `chapter_idx` | The index and placement of the chapter in the video (e.g., the first chapter has index `0`).  |

| Partition  | # Examples    |
|------------|--------------|
| Training   | 146,907 (84.8%)|
| Validation | 13,206 (7.6%)  |
| Testing    | 13,082 (7.6%)  |
| Total      | 173,195       |

### Video Data

A download script for the video and audio data is provided.

```py
python download_videos.py
```

In the script, you can further specify a target folder (default is `./video`) and target formats in a priority list. 

## Loading Data

The dataset can be loaded directly using the HuggingFace `datasets` library:

```py
from datasets import load_dataset

# Load the audio config (with embedded audio)
dataset = load_dataset("retkowski/ytseg", "audio", split="test")

# Load the text config (text-only)
dataset = load_dataset("retkowski/ytseg", "text", split="test")

# Load the titles config
dataset = load_dataset("retkowski/ytseg", "titles", split="test")
```

**Note on Binary Labels:** The binary segmentation labels (e.g., `target_binary_ref`) are prefixed with `|=` to force the field to be stored as a string, preventing leading zeros from being lost during processing. For actual usage, strip the `|=` prefix:

```py
binary_labels = dataset['target_binary_ref'].lstrip('|=')
```

## Citing

We kindly request you to cite our corresponding EACL 2024 paper if you use our dataset.

```
@inproceedings{retkowski-waibel-2024-text,
    title = "From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptions",
    author = "Retkowski, Fabian  and Waibel, Alexander",
    editor = "Graham, Yvette  and Purver, Matthew",
    booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2024",
    address = "St. Julian{'}s, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.eacl-long.25",
    pages = "406--419",
    abstract = "Text segmentation is a fundamental task in natural language processing, where documents are split into contiguous sections. However, prior research in this area has been constrained by limited datasets, which are either small in scale, synthesized, or only contain well-structured documents. In this paper, we address these limitations by introducing a novel benchmark YTSeg focusing on spoken content that is inherently more unstructured and both topically and structurally diverse. As part of this work, we introduce an efficient hierarchical segmentation model MiniSeg, that outperforms state-of-the-art baselines. Lastly, we expand the notion of text segmentation to a more practical {``}smart chaptering{''} task that involves the segmentation of unstructured content, the generation of meaningful segment titles, and a potential real-time application of the models.",
}
```
## Changelog

- 20.01.2025 -- Major data and format update:
  - Added ASR transcripts (Whisper-tiny and Whisper-large), structured transcript targets with timestamps, and metadata for finer-grained analysis (speaker category, dominant speaker proportion, number of speakers, duration)
  - Added audio config with HuggingFace Audio feature for seamless loading with embedded audio
  - Updated to use HuggingFace `datasets` library for data loading (replacing local pandas scripts and use proper HF configs)
  - Updated `YTSeg[Titles]` field names for clarity
- 25.07.2024 -- Added complete list of chapter titles to `YTSeg` (`YTSeg[Titles]` is a filtered subset)
- 09.04.2024 -- Added audio data
- 27.02.2024 -- Initial release

## License

The dataset is available under the **Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) 4.0** license. We note that we do not own the copyright of the videos and as such opted to release the dataset with a non-commercial license, with the intended use to be in research and education.