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

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 1 .