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Meetings play a critical infrastructural role in the coordination of work. Inrecent years, due to shift to hybrid and remote work, more meetings are movingto online Computer Mediated Spaces. This has led to new problems (e.g. moretime spent in less engaging meetings) and new opportunities (e.g. automatedtranscription/captioning and recap support). Recent advances in large languagemodels (LLMs) for dialog summarization have the potential to improve theexperience of meetings by reducing individuals' meeting load and increasing theclarity and alignment of meeting outputs. Despite this potential, they facetechnological limitation due to long transcripts and inability to capturediverse recap needs based on user's context. To address these gaps, we design,implement and evaluate in-context a meeting recap system. We firstconceptualize two salient recap representations -- important highlights, and astructured, hierarchical minutes view. We develop a system to operationalizethe representations with dialogue summarization as its building blocks.Finally, we evaluate the effectiveness of the system with seven users in thecontext of their work meetings. Our findings show promise in using LLM-baseddialogue summarization for meeting recap and the need for both representationsin different contexts. However, we find that LLM-based recap still lacks anunderstanding of whats personally relevant to participants, can miss importantdetails, and mis-attributions can be detrimental to group dynamics. We identifycollaboration opportunities such as a shared recap document that a high qualityrecap enables. We report on implications for designing AI systems to partnerwith users to learn and improve from natural interactions to overcome thelimitations related to personal relevance and summarization quality.

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