Meetalk: Retrieval-Augmented and Adaptively Personalized Meeting Summarization with Knowledge Learning from User Corrections
Zheng Chen, Jiang Futian, Yue Deng, Changyang He, Bo Li · 2025
We present Meetalk, a retrieval-augmented and knowledge-adaptive system for generating personalized meeting minutes.Although large language models (LLMs) excel at summarizing, their output often lacks faithfulness and does not reflect user-specific structure and style.Meetalk addresses these issues by integrating ASR-based transcription with LLM generation guided by user-derived knowledge.Specifically, Meetalk maintains and updates three structured databases, Table of Contents, Chapter Allocation, and Writing Style, based on user-uploaded samples and editing feedback.These serve as a dynamic memory that is retrieved during generation to ground the model's outputs.To further enhance reliability, Meetalk introduces hallucination-aware uncertainty markers that highlight low-confidence segments for user review.In a user study in five real-world meeting scenarios, Meetalk significantly outperforms a strong baseline (iFLYTEK ASR + ChatGPT-4o) in completeness, contextual relevance, and user trust.Our findings underscore the importance of knowledge foundation and feedback-driven adaptation in building trustworthy, personalized LLM systems for high-stakes summarization tasks.