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New multi-agent framework ReSUM enhances meeting summarization

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced ReSUM, a novel multi-agent framework designed to improve query-focused summarization (QFS) for long meeting transcripts. Unlike existing end-to-end methods, ReSUM employs a modular approach with a planner that coordinates specialized retrieval and summarization agents. This framework enhances robustness for long contexts and minimizes irrelevant information by distilling teacher reasoning into query- and plan-conditioned components. Experiments demonstrate that ReSUM achieves consistent improvements on QFS benchmarks for meetings. AI

IMPACT This framework could improve the efficiency and accuracy of summarizing lengthy meeting transcripts, benefiting professionals who need to quickly extract key information.

RANK_REASON The cluster describes a new research paper introducing a novel framework for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New multi-agent framework ReSUM enhances meeting summarization

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The cluster describes a new research paper introducing a novel framework for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Asif Ekbal ·

    ReSUM: Reasoning-Driven Multi-Agent Framework for Query-Focused Summarization

    LLMs excel at language tasks, but long-meeting QFS remains difficult due to complex speaker dynamics, sparse key information, and limited data. Meetings involve disfluencies, long transcripts and complex discourse. QFS have high risk of information omission, hallucination, even f…

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