You need to MIMIC to get FAME: Solving Meeting Transcript Scarcity with a Multi-Agent Conversations

TL;DR

FAME is a new large-scale dataset of synthetic meeting transcripts generated by a multi-agent framework, enabling improved research in meeting summarization and conversation analysis despite limited real data.

Contribution

The paper introduces FAME, a scalable, multi-lingual synthetic meeting dataset created using MIMIC, with a novel framework for generating realistic, psychologically grounded dialogues.

Findings

FAME achieves high naturalness scores (4.5/5) in human assessments.

The dataset preserves speaker-centric challenges (3/5) and introduces richer difficulty levels (4/5).

FAME serves as an effective proxy for real meetings, facilitating new research scenarios.

Abstract

Meeting summarization suffers from limited high-quality data, mainly due to privacy restrictions and expensive collection processes. We address this gap with FAME, a dataset of 500 meetings in English and 300 in German produced by MIMIC, our new multi-agent meeting synthesis framework that generates meeting transcripts on a given knowledge source by defining psychologically grounded participant profiles, outlining the conversation, and orchestrating a large language model (LLM) debate. A modular post-processing step refines these outputs, mitigating potential repetitiveness and overly formal tones, ensuring coherent, credible dialogues at scale. We also propose a psychologically grounded evaluation framework assessing naturalness, social behavior authenticity, and transcript difficulties. Human assessments show that FAME approximates real-meeting spontaneity (4.5/5 in naturalness), preserves speaker-centric challenges (3/5 in spoken language), and introduces richer information-oriented difficulty (4/5 in difficulty). These findings highlight that FAME is a good and scalable proxy for real-world meeting conditions. It enables new test scenarios for meeting summarization research and other conversation-centric applications in tasks requiring conversation data or simulating social scenarios under behavioral constraints.