Multi-Agent Systems for Action Item Extraction from Meeting Transcripts: A Comprehensive Review

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Multi-Agent Systems for Action Item Extraction from Meeting Transcripts: A Comprehensive Review

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  • Volume : 3 Issue : 2 2025
  • Page Number : 33-39
  • Publication : ISRDO

Published Manuscript

Title

Multi-Agent Systems for Action Item Extraction from Meeting Transcripts: A Comprehensive Review

Author

1. Haruto Tanaka, Student, Kansai Institute of Advanced Science and Technology, Japan
2. Grant Thompson, Professor, Kansai Institute of Advanced Science and Technology, Japan

Abstract

The rapid growth of digital collaboration platforms has led to an unprecedented increase in recorded meetings and conversational data. These interactions are commonly preserved as textual transcripts through automated transcription technologies. While transcripts provide a complete record of discussions, their unstructured and verbose nature limits their direct usefulness for organizational follow-up and decision-making. Among the most valuable outcomes of meetings are action items, which capture tasks, responsibilities, and commitments that must be executed after the discussion ends. This review paper examines the role of multi-agent artificial intelligence systems in extracting action items from meeting transcripts. By synthesizing recent advances in multi-agent frameworks, large language models, conversational analysis, and meeting intelligence, the paper highlights how agent-based decomposition improves accuracy, interpretability, and scalability in action item extraction. The review also discusses architectural patterns, coordination strategies, and application contexts, offering a structured understanding of how multi-agent approaches address the limitations of traditional single-model pipelines.    

Keywords

Multi-agent systems meeting transcripts action item extraction large language models document intelligence conversational AI

Conclusion

Multi-agent systems offer a robust and conceptually well-aligned approach for extracting action items from meeting transcripts by distributing the complex task of conversational understanding across specialized, cooperating agents. Unlike traditional single-model summarization pipelines, multi-agent frameworks enable deeper reasoning about intent, commitment, and context, which are essential for accurately identifying actionable outcomes embedded within unstructured dialogue. By separating comprehension, intent detection, validation, and consolidation into distinct yet coordinated processes, these systems reduce ambiguity, improve interpretability, and enhance the reliability of extracted action items.

This review highlights that the true strength of multi-agent architectures lies not only in task decomposition but also in their coordination and reasoning strategies, which mirror human analytical practices such as iterative review, cross-checking, and contextual refinement. When applied to meeting transcripts, such capabilities support clearer accountability, more effective follow-up, and stronger alignment between conversational decisions and organizational execution. As conversational data continues to grow in scale and importance across professional, academic, and high-stakes domains, multi-agent action item extraction is positioned to become a foundational component of next-generation meeting intelligence and document understanding systems, bridging the gap between discussion and decisive action.

Author Contrubution

The author confirms sole responsibility for the following: study conception and design, data collection, analysis and interpretation of results, and manuscript preparation.

Funding

The authors did not receive any specific grants from funding agencies in the public, commercial, or non-profit sectors for the research, authorship, and/or publication of this article.

Conflict of Interest

All authors declare the absence of any conflicts of interest.

Data Sharing Statement

Not applicable.


Software And Tools Use

Not applicable.

Acknowledgements

I am grateful for the expertise and help provided by all who contributed to this study and manuscript, and for the comments from anonymous reviewers.

Corresponding Author

HT
Haruto Tanaka

Kansai Institute of Advanced Science and Technology, Student, Japan

GT
Grant Thompson

Kansai Institute of Advanced Science and Technology, Professor, Japan

Copyright

Copyright: ©2026 Corresponding Author. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Tanaka, Haruto, and Thompson, Grant. “Multi-Agent Systems for Action Item Extraction from Meeting Transcripts: A Comprehensive Review.” Scientific Research Journal of Science, Engineering and Technology, vol. 3, no. 2, 2026, pp. 33-39, https://isrdo.org/journal/SRJSET/currentissue/multi-agent-systems-for-action-item-extraction-from-meeting-transcripts-a-comprehensive-review

Tanaka, H., & Thompson, G. (2026). Multi-Agent Systems for Action Item Extraction from Meeting Transcripts: A Comprehensive Review. Scientific Research Journal of Science, Engineering and Technology, 3(2), 33-39. https://isrdo.org/journal/SRJSET/currentissue/multi-agent-systems-for-action-item-extraction-from-meeting-transcripts-a-comprehensive-review

Tanaka Haruto and Thompson Grant, Multi-Agent Systems for Action Item Extraction from Meeting Transcripts: A Comprehensive Review, Scientific Research Journal of Science, Engineering and Technology 3, no. 2(2026): 33-39, https://isrdo.org/journal/SRJSET/currentissue/multi-agent-systems-for-action-item-extraction-from-meeting-transcripts-a-comprehensive-review

2540

Total words

1095

Unique Words

110

Sentence

22.6

Avg Sentence Length

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Subjectivity

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Text Statistics

  • Flesch Reading Ease : 2.14
  • Smog Index : 17.4
  • Flesch Kincaid Grade : 17.5
  • Coleman Liau Index : 21.46
  • Automated Readability Index : 20.8
  • Dale Chall Readability Score : 9.45
  • Difficult Words : 617
  • Linsear Write Formula : 19.75
  • Gunning Fog : 13.47
  • Text Standard : 17th and 18th grade

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