TY - M-10444
AU - Tanaka, Haruto
AU - Thompson, Grant
TI - Multi-Agent Systems for Action Item Extraction from Meeting Transcripts: A Comprehensive Review
T2 - Scientific Research Journal of Science, Engineering and Technology

PY - 2025
VL - 3
IS - 2
SN - 2584-0584

AB - 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.    

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