Datasets:
license: cc-by-nc-4.0
language:
- en
tags:
- text segmentation
- smart chaptering
- segmentation
- youtube
pretty_name: YTSeg
size_categories:
- 10K<n<100K
YTSeg
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.
Data Overview
YTSeg
Each video is represented as a JSON object with the following fields:
| Field | Description |
|---|---|
text |
A flat list of sentences. |
targets |
The target segmentation as string of binary values (e.g., 000100000010). |
channel_id |
The YouTube channel ID which this video belongs to. |
video_id |
The YouTube video ID. |
| Partition | # Examples |
|---|---|
| Training | 16,404 (85%) |
| Validation | 1,447 (7.5%) |
| Testing | 1,448 (7.5%) |
| Total | 19,229 |
YTSeg[Titles]
Each chapter of a video is represented as a JSON object with the following fields:
| Field | Description |
|---|---|
input |
The complete chapter/section text. |
input_with_chapters |
The complete chapter/section text with previous section titles prepended. |
target |
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 & Audio Data
A download script for the video and audio data is provided.
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
This repository comes with a simple, examplary script to read in the data with pandas.
from load_data import get_partition
test_data = get_partition('test')
Equivalently, to read in YTSeg[Titles]:
from load_data import get_title_partition
test_data = get_title_partition('test')
Citing
TBD
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.