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

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arxiv 2307.15793 v3 pith:FCQ3TPGZ submitted 2023-07-28 cs.HC cs.AIcs.IR

classification cs.HCcs.AIcs.IR
keywords recapmeetingsworkcontextdesigndesignsdialoguediscourse
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. More AI Assistance Reduces Cognitive Engagement: Examining the AI Assistance Dilemma in AI-Supported Note-Taking

    cs.HC 2025-09 conditional novelty 6.0 of 10

    In a 30-person within-subject study, moderate AI note assistance produced the highest comprehension, while fully automated notes produced the lowest despite being preferred.

  2. MEETING DELEGATE: Benchmarking LLMs on Attending Meetings on Our Behalf

    cs.CL 2025-02 conditional novelty 6.0 of 10

    LLM meeting delegates achieve about 60% loose recall on a new benchmark built from real meeting transcripts, with GPT-4/4o most balanced.

  3. MeetMap: Real-Time Collaborative Dialogue Mapping with LLMs in Online Meetings

    cs.HC 2025-02 conditional novelty 6.0 of 10

    A real-time collaborative dialogue mapping system with two levels of AI assistance improved meeting participants' sense-making and consensus compared to a transcript-plus-notes baseline.

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