Datasets:
Added complete list of chapter titles to YTSeg
Browse files- README.md +6 -0
- data/partitions/yt_seg.test.json +2 -2
- data/partitions/yt_seg.train.json +2 -2
- data/partitions/yt_seg.val.json +2 -2
- data/yt_seg.json +2 -2
README.md
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@@ -30,6 +30,7 @@ Each video is represented as a JSON object with the following fields:
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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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| `audio_path` | Path to the .mp3 file of the video |
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| Partition | # Examples |
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|------------|--------------|
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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.",
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}
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```
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## License
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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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| `audio_path` | Path to the .mp3 file of the video |
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| `chapters` | A list of chapter titles corresponding to each segment |
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| Partition | # Examples |
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|------------|--------------|
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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.",
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}
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```
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## Changelog
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- 25.07.2024 -- Added complete list of chapter titles to `YTSeg` (`YTSeg[Titles]` is a filtered subset)
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- 09.04.2024 -- Added audio data
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- 27.02.2024 -- Initial release
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## License
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data/partitions/yt_seg.test.json
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data/partitions/yt_seg.train.json
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data/partitions/yt_seg.val.json
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data/yt_seg.json
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