mlsanityData to 14 Oct 2026

Abstractive Meeting Summarization: A Survey

arXiv 2208.04163 ↗First posted 8 Aug 2022cs.CLcited by 9

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Abstract (arXiv)

A system that could reliably identify and sum up the most important points of a conversation would be valuable in a wide variety of real-world contexts, from business meetings to medical consultations to customer service calls. Recent advances in deep learning, and especially the invention of encoder-decoder architectures, has significantly improved language generation systems, opening the door to improved forms of abstractive summarization, a form of summarization particularly well-suited for multi-party conversation. In this paper, we provide an overview of the challenges raised by the task of abstractive meeting summarization and of the data sets, models and evaluation metrics that have been used to tackle the problems.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 67 of its 158 references that are in the map. First posted on arXiv 8 Aug 2022.

Builds on

67 of 158 references.

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50 most cited of 67 All 67 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 2; 2025: 1; 2026: 2.

9 papers.

  1. 2023Summaries, Highlights, and Action items: Design, implementation and evaluation of an LLM-powered meeting recap systemcited by 12
  2. 2023Adapter-based Selective Knowledge Distillation for Federated Multi-domain Meeting Summarizationcited by 6
  3. 2024Sentence-wise Speech Summarization: Task, Datasets, and End-to-End Modeling with LM Knowledge Distillationcited by 3
  4. 2023Agreement Tracking for Multi-Issue Negotiation Dialoguescited by 2
  5. 2023The Claire French Dialogue Datasetcited by 1
  6. 2024Instant Answering in E-Commerce Buyer-Seller Messaging using Message-to-Question Reformulationcited by 0
  7. 2025A Novel Trustworthy Video Summarization Algorithm Through a Mixture of LoRA Expertscited by 0
  8. 2026A Multi-Model Metric-based Selection Framework for Abstractive Text summarizationcited by 0
  9. 2026Segment-level Tree Search for Long Meeting Document Summarizationcited by 0