mlsanityData to 7 Oct 2026

Meeting Summarization: A Survey of the State of the Art

arXiv 2212.08206 ↗First posted 16 Dec 2022cs.CLcited by 3

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Abstract (arXiv)

Information overloading requires the need for summarizers to extract salient information from the text. Currently, there is an overload of dialogue data due to the rise of virtual communication platforms. The rise of Covid-19 has led people to rely on online communication platforms like Zoom, Slack, Microsoft Teams, Discord, etc. to conduct their company meetings. Instead of going through the entire meeting transcripts, people can use meeting summarizers to select useful data. Nevertheless, there is a lack of comprehensive surveys in the field of meeting summarizers. In this survey, we aim to cover recent meeting summarization techniques. Our survey offers a general overview of text summarization along with datasets and evaluation metrics for meeting summarization. We also provide the performance of each summarizer on a leaderboard. We conclude our survey with different challenges in this domain and potential research opportunities for future researchers.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 43 of its 101 references that are in the map. First posted on arXiv 16 Dec 2022.

Builds on

43 of 101 references.

References · 43

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31 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 1.

3 papers.

  1. 2024A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methodscited by 101
  2. 2023Adapter-based Selective Knowledge Distillation for Federated Multi-domain Meeting Summarizationcited by 6
  3. 2026Dialogue Summarization with Emotion Dynamics Using Topic- and Participant-Centric Decompositioncited by 0