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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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tags: |
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- text segmentation |
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- smart chaptering |
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- segmentation |
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- youtube |
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pretty_name: YTSeg |
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size_categories: |
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- 10K<n<100K |
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--- |
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# YTSeg |
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We present `YTSeg`, 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 for further information. |
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## Data Overview |
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### YTSeg |
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Each video is represented as a JSON object with the following fields: |
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| Field | Description | |
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|--------------|------------------------------------------------| |
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| `text` | A flat list of sentences. | |
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| `targets` | The target segmentation as string of binary values (e.g., `000100000010`). | |
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| `channel_id` | The YouTube channel ID which this video belongs to. | |
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| `video_id` | The YouTube video ID. | |
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| Partition | # Examples | |
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|------------|--------------| |
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| Training | 16,404 (85%) | |
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| Validation | 1,447 (7.5%) | |
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| Testing | 1,448 (7.5%) | |
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| Total | 19,229 | |
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### YTSeg[Titles] |
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Each chapter of a video is represented as a JSON object with the following fields: |
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| Field | Description | |
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|--------------|------------------------------------------------| |
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| `input` | The complete chapter/section text. | |
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| `input_with_chapters` | The complete chapter/section text with previous section titles prepended. | |
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| `target` | The target chapter title. | |
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| `channel_id` | The YouTube channel ID which this chapter's video belongs to. | |
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| `video_id` | The YouTube video ID which this chapter belongs to. | |
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| `chapter_idx` | The index and placement of the chapter in the video (e.g., the first chapter has index `0`). | |
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| Partition | # Examples | |
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|------------|--------------| |
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| Training | 146,907 (84.8%)| |
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| Validation | 13,206 (7.6%) | |
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| Testing | 13,082 (7.6%) | |
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| Total | 173,195 | |
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### Video & Audio Data |
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A download script for the video and audio data is provided. |
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```py |
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python download_videos.py |
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``` |
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In the script, you can further specify a target folder (default is `./video`) and target formats in a priority list. |
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## Loading Data |
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This repository comes with a simple, examplary script to read in the data with `pandas`. |
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```py |
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from load_data import get_partition |
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test_data = get_partition('test') |
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``` |
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Equivalently, to read in `YTSeg[Titles]`: |
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```py |
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from load_data import get_title_partition |
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test_data = get_title_partition('test') |
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``` |
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## Citing |
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TBD |
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## License |
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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. |
