Dialogue acts enhanced extract–abstract framework for meeting summarization

Shichao Sun, Ruifeng Yuan, Wenjie Li, Ziqiang Cao, Sujian Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Meeting summarization is challenging due to complex multi-party interactions with spontaneous utterances. This paper tries to mitigate this challenge by leveraging dialogue acts (DAs). We propose Dialogue Acts enhanced extract–abstract framework for meeting Summarization (DASum). We jointly train meeting summarization with DAs classification to enable our model get aware of DAs and better understand dialogue interactions. Moreover, our DASum model uses DAs to measure the salience of an utterance in the extractor and as prefix prompt to guide how to integrate extracted utterances in the abstractor. We conducted experiments on two public datasets, ICSI (for meeting summarization) and QMSum (for query-based meeting summarization), which compress the meeting transcripts (average 9000 tokens) into a general summary (no more than 700 tokens) or a specific summary (no more than 150 tokens) for a query. Experimental results show our DASum achieves promising results on the ICSI (46.41/ 11.52/ 43.82/ 5.86) and QMSum (36.37/ 11.71/ 32.07/ 19.18) datasets in terms of ROUGE-1/ ROUGE-2/ ROUGE-L/ BERTScore.

Original languageEnglish
Article number103635
JournalInformation Processing and Management
Volume61
Issue number3
DOIs
Publication statusPublished - May 2024

Keywords

  • Deep learning
  • extract–abstract framework
  • Meeting summarization
  • Natural language processing

ASJC Scopus subject areas

  • Information Systems
  • Media Technology
  • Computer Science Applications
  • Management Science and Operations Research
  • Library and Information Sciences

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