You need to MIMIC to get FAME : Solving Meeting Transcript Scarcity with Multi-Agent Conversations (2025)

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 points in difficulty). These findings show 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.

## introduction meetings underlie collaboration and decisionmaking in corporations, academia, and government. meeting summaries help record key discussion points, update absentees, and capture to-dos @xcite @xcite . while ai-based summaries are available on platforms such as zoom foot_1 , microsoft teams foot_2 , they typically build on limited english data that does not represent the diversity of real meetings, e.g., multilingual sessions, specialized discussions @xcite . data scarcity for training and testing meeting summarization systems persists due to privacy and intellectual property concerns, along with expensive manual annotation @xcite . existing corpora, such as ami @xcite , icsi @xcite , and meetingbank @xcite , offer only a narrow range of scenarios, which primarily revolve around staged business, academic, or parliamentary meetings. non-english resources like fredsum @xcite , containing manually transcribed and annotated political debates, remain sporadic and underscore the lack of linguistic diversity. researchers have explored synthetic transcripts to address this scarcity, but many methods are suboptimal in scalability and realism. single-model, omniscient continuation @xcite can produce dialogues lacking actual knowledge and speaker interplay, while crowdsourced role-plays are expensive @xcite . automated heuristics (e.g., noising, swapping) often yield disjointed conversations @xcite @xcite . these approaches struggle to balance large-scale generation with authentic group dynamics and credible topic evolution, even though they can process and generate thousands of tokens. we introduce mimic (multi-agent imitation of conversations, see figure 1 ), a movie-productioninspired framework based on multi-agent debate @xcite . mimic summarizes a knowledge source, expands the summary into an agenda, and orchestrates psychologically grounded agents debating turn-by-turn, allowing interruptions (e.g., phone calls). a modular refinement step mitigates repetitions or overly formal speech, ensuring plausible and coherent discourse at scale. with mimic, we generate fame (fake meetings), a corpus of 800 meetings (500 english, 300 german parallel in input) covering 14 meeting types (e.g., project updates, brainstorming) and 300 wikipedia articles as a knowledge source. we evaluate the synthetic meetings on quality criteria, e.g., naturalness, coherence, @xcite , and transcript difficulties, e.g., scattered information @xcite , and propose a new psychology-based measure of behavioral authenticity @xcite . human ratings confirm that fame achieves near-real spontaneity (4.5/5 in naturalness), preserves speaker-related challenges (3/5 in spoken language), and intensifies low-information-density difficulties @xcite . comparisons with real transcripts and 100 crowdsourced meeting experiences show a similar behavioral pattern. evaluations of gpt-4o (openai, 2024), gemini 1.5 pro @xcite , deepseek-r1 distill llama 70b (deepseek-ai and zhang, 2025), llama 3.3 70b foot_3 (llama team, 2024) reveal persistent context-handling issues. ablation studies show that mimic reliably generates good transcripts using different inputs or llms. ## the mimic methodology we propose mimic, a multi-agent framework inspired by movie production to generate synthetic meeting transcripts from a knowledge source through multi-agent debate (see figure 1 ). the basic idea is to summarize the knowledge source to distill its key highlights to be discussed in a multiagent-llm setup. this setup emulates a discussion among participants with distinct personas and their private memory of the knowledge source, including real-world dynamics such as turn-taking, disagreements, clarifications, and topic continuity. mimic operates in three phases, i.e., pre-production, production, and post-production, with seven stages overall (splitted 3/2/2 between each phase). we explain here the methodological background of mimic, the corresponding prompts, and implementation details are covered in appendices c and h. in appendix i, we present the result of each stage using an example knowledge source. ## the fame dataset in the following, we detail how fame was generated, present its overall statistics, and analyze the authenticity of its simulated meetings and how challenging they are compared to real transcripts. all data and annotations performed here are available per appendix a. ## baseline models and results we evaluate current llms on abstractive meeting summarization for real and synthetic transcripts, sampling 30 meetings from each fame (english, german) and qmsum (90 total). ## final considerations we introduced mimic, a seven-stage multi-agent framework that uses psychologically grounded, non-omniscient llms to generate source-grounded meeting transcripts. mimic generated fame, a multilingual corpus of 500 english and 300 german meetings on 28 domains and 14 meeting types. human assessments showed that fame closely mirrors real meetings (4.5/5 in naturalness) and amplified low-information density (4/5). comparisons with real meetings suggest that fame captures authentic group dynamics, while our ablation studies highlighted how varying knowledge sources and backbone models shape transcript quality and demonstrate the gpt's reliable role enactment. fame and its detailed annotations open new directions for meeting summarization, from multilingual model development to re-introducing finetuning for llms and reinforcement learning to address persistent shortcomings (section 5). by releasing mimic as open-source, we provide a powerful toolkit that researchers can adapt to low-resource languages, diverse domains, and a range of conversational styles. the framework's psychology-based behavior definitions and evaluation methodology bring a higher level of realism into synthetic conversations, enabling deeper investigations of social dynamics. our work bridges a data gap through human-like meeting simulations that foster advances in summarization, conversational ai, social simulation, and beyond.