mlsanityData to 13 Oct 2026

Summaries, Highlights, and Action items: Design, implementation and evaluation of an LLM-powered meeting recap system

arXiv 2307.15793 ↗First posted 28 Jul 2023cs.HC cs.AI cs.IRcited by 12

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Abstract (arXiv)

Meetings play a critical infrastructural role in coordinating work. The recent surge of hybrid and remote meetings in computer-mediated spaces has led to new problems (e.g., more time spent in less engaging meetings) and new opportunities (e.g., automated transcription/captioning and recap support). Advances in dialogue summarization offer the potential for improving post-meeting experiences, but fixed-length summaries often fail to meet diverse needs, such as quick overviews or detailed insights. To address these gaps, we use cognitive science and discourse theories to conceptualize two recap designs: important highlights and a structured, hierarchical minutes view, targeting complementary recap needs. We operationalize these representations into high-fidelity prototypes using dialogue summarization. Finally, we evaluate the representations' effectiveness with seven users in the context of their work meetings at Microsoft. Our results show both recap types are valuable in different contexts, enabling collaboration through discussions and consensus-building. Exploring the meaning of users adding, editing, and deleting from recaps suggests varying alignment for using these actions to improve AI-recap. Our design implications, such as incorporating organizational artifacts (e.g., linking presentations) in recaps and personalizing context, advance the discourse of effective recap designs for organizational work and support past results from cognition studies.

Key facts. Cited by 12 papers in the map (data through 13 Oct 2026). Builds on 18 of its 113 references that are in the map. First posted on arXiv 28 Jul 2023.

Builds on

18 of 113 references.

References · 18

  1. 2017Attention Is All You Needcited by 60,106
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,884
  3. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,171
  4. 2019SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarizationcited by 533
  5. 2021DialogSum: A Real-Life Scenario Dialogue Summarization Datasetcited by 182
  6. 2017A Survey on Dialogue Systems: Recent Advances and New Frontierscited by 162
  7. 2017Text Summarization Techniques: A Brief Surveycited by 59
  8. 2019Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processescited by 53
  9. 2022Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarizationcited by 29
  10. 2020Investigation of End-To-End Speaker-Attributed ASR for Continuous Multi-Talker Recordingscited by 28
  11. 2021An Exploratory Study on Long Dialogue Summarization: What Works and What's Nextcited by 28
  12. 2020Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systemscited by 16
6 more
  1. 2021A Sliding-Window Approach to Automatic Creation of Meeting Minutescited by 14
  2. 2021A Bag of Tricks for Dialogue Summarizationcited by 13
  3. 2022User-Driven Research of Medical Note Generation Softwarecited by 13
  4. 2022Abstractive Meeting Summarization: A Surveycited by 9
  5. 2018A Survey on Neural Network-Based Summarization Methodscited by 6
  6. 2022Taxonomy of Abstractive Dialogue Summarization: Scenarios, Approaches and Future Directionscited by 3

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 6; 2026: 6.

12 papers.