Han Wu, Mingjie Zhan, Haochen Tan, Zhaohui Hou, Ding Liang and 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.

The files started with long_ contain the overall meeting summaries, and the files started with short_ contains the segmentation meeting summaries and corresponding headlines.

We put all meeting transcripts in overall_context.txt and all highlight annotations in overall_highlights.txt. All files share the id and av_num values.

The long/short_train/dev/text.txt files contain the annotations of topic segmentation, headlines, segmentation summary and overall meeting summary. The keys in the dict are:

• id and av_num: the unique identifiers of the meeting transcript.
• eos_index: the utterance positions of topic segmentation.
• speaker: the speaker identifier.
• context: the meeting transcript.
• summary: the overall meeting summary.
• discussion: the segmentation summary.
• agenda: the segmentation headline.

The highlight files provide the annotations of highlight sentences.

• id and av_num: the unique identifier of the meeting transcript.
• highlights: the 0/1 list of context words. 1 stands for the highlighted word.

@inproceedings{wu-etal-2023-vcsum,
    title = "{VCSUM}: A Versatile {C}hinese Meeting Summarization Dataset",
    author = "Wu, Han and Zhan, Mingjie and Tan, Haochen and Hou, Zhaohui and Liang, Ding and Song, Linqi",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = July,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.377",
    pages = "6065--6079"
}