VCSUM: A Versatile Chinese Meeting Summarization Dataset

Han Wu, Mingjie Zhan, Haochen Tan, Zhaohui Hou, Ding Liang, Linqi Song


Abstract
Compared to news and chat summarization, the development of meeting summarization is hugely decelerated by the limited data. To this end, we introduce a versatile Chinese meeting summarization dataset, dubbed VCSum, consisting of 239 real-life meetings, with a total duration of over 230 hours. We claim our dataset is versatile because we provide the annotations of topic segmentation, headlines, segmentation summaries, overall meeting summaries, and salient sentences for each meeting transcript. As such, the dataset can adapt to various summarization tasks or methods, including segmentation-based summarization, multi-granularity summarization and retrieval-then-generate summarization. Our analysis confirms the effectiveness and robustness of VCSum. We also provide a set of benchmark models regarding different downstream summarization tasks on VCSum to facilitate further research.
Anthology ID:
2023.findings-acl.377
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6065–6079
Language:
URL:
https://aclanthology.org/2023.findings-acl.377
DOI:
10.18653/v1/2023.findings-acl.377
Bibkey:
Cite (ACL):
Han Wu, Mingjie Zhan, Haochen Tan, Zhaohui Hou, Ding Liang, and Linqi Song. 2023. VCSUM: A Versatile Chinese Meeting Summarization Dataset. In Findings of the Association for Computational Linguistics: ACL 2023, pages 6065–6079, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
VCSUM: A Versatile Chinese Meeting Summarization Dataset (Wu et al., Findings 2023)
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PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2023.findings-acl.377.pdf