arXiv paper

README.md

@@ -14,7 +14,7 @@ size_categories:
 
 # 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.
+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](https://arxiv.org/abs/2402.17633) for further information.
 
 ## Data Overview
 
@@ -85,7 +85,16 @@ test_data = get_title_partition('test')
 
 ## Citing
 
-TBD
+We kindly request you to cite our corresponding paper (accepted to EACL 2024) if you use our dataset.
+
+```
+@article{retkowski2024ytseg,
+  title={From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptions},
+  author={Retkowski, Fabian and Waibel, Alexander},
+  journal={arXiv preprint arXiv:2402.17633},
+  year={2024}
+}
+```
 
 ## License