Meetalk: Retrieval-Augmented and Adaptively Personalized Meeting Summarization with Knowledge Learning from User Corrections

Zheng CHEN, JIANG FUTIAN, Yue Deng, Changyang He, Bo Li

Keywords: large language models, retrieval-augmented generation, knowledge grounding, personalization, hallucination detection, user-adaptive systems

TL;DR: We propose Meetalk, a retrieval-augmented system that learns from user feedback to generate personalized and hallucination-aware meeting minutes using structured knowledge guidance.

Abstract: 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.

Archival Status: Archival (included in proceedings)

Submission Number: 52