Meetalk: Retrieval-Augmented and Adaptively Personalized Meeting Summarization with Knowledge Learning from User Corrections (2025)

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.

## introduction large language models (llms) have shown impressive capabilities in performing summarization and generation tasks across a wide range of domains. however, a fundamental question remains: how do llms utilize knowledge, unstructured, in real-world applications, and how can we ensure that this knowledge is personalized, accurate, and faithful? this question is especially critical in the context of automated meeting minutes generation, where information needs to be not only complete and concise but also aligned with domain-specific writing norms and user preferences. although existing approaches have used llm to generate abstractive meeting summaries, they often fall short in two key areas: (1) the inability to adapt to user-specific structural and stylistic knowledge and (2) the tendency to produce hallucinated or generic outputs due to weak grounding. furthermore, traditional systems lack mechanisms for learning from user feedback, leading to repeated errors and suboptimal long-term performance in repetitive meeting contexts. in this work, we propose meetalk, an adaptive meeting minutes generation system that addresses these challenges by tightly integrating retrievalaugmented generation (rag), user-driven knowledge modeling, and hallucination-aware design. specifically, meetalk builds and updates structured knowledge bases, including chapter allocation mappings and writing style templates, learning from user-provided examples and edits. at inference time, these personalized knowledge modules are retrieved and injected into llm prompts to guide faithful and stylistically consistent generation. in addition, we incorporate uncertainty indicators such as "[not sure]" labels to make the confidence of the model interpretable to users, thus enabling human-ai collaboration in mitigating hallucinated content. to evaluate meetalk, we conducted a controlled user study in five real-world meeting scenarios. compared to a strong baseline (iflytek asr + chatgpt-4o), meetalk consistently improves output completeness, contextual relevance, and user trust, while significantly reducing time and cognitive load. our findings suggest that adaptively modeling and utilizing user-specific knowledge not only enhances generation quality, but also provides a promising paradigm for deploying trustworthy, personalized llm-based systems in professional workflows. ## knowledge integration and utilization in meetalk in the era of large language models (llms), the ability to effectively ground generation on structured and personalized knowledge is crucial to enhancing output accuracy and trustworthiness. meetalk addresses this challenge by incorporating a retrieval-augmented and user-adaptive knowledge pipeline into its summarization workflow. this section details how meetalk constructs, retrieves, and updates knowledge to enable personalized, faithful, and hallucination-aware meeting minutes generation. ## evaluation to evaluate the effectiveness of meetalk in supporting the generation of meeting minutes, we conducted a within-subject study comparing meetalk with the conventional approach to automate meeting minutes. as our baseline, we selected iflytek real-time asr combined with chatgpt-4o. participants were asked to complete two tasks, using the baseline method and meetalk respectively. to validate the optimization of our system for handling the repetitive nature of meetings, participants in each task processed three meeting audios from a specific scenario, generating meeting minutes in a consistent format. to assess the generalizability of meetalk, we selected five different scenarios and invited participants who were familiar with these scenarios to complete the tasks. through these comparisons, we seek to evaluate whether meetalk outperforms the baseline method in addressing the design goals derived from literature and the formative studies. eighteen (@xmath0, with five different real-world scenarios included: legal consultations, study abroad counseling, academic discussions, mock interviews, and company pitches. participants generally span moderate to high levels of expertise within their respective fields. it is noteworthy that all participants demonstrate high frequency of meetings and substantial usage of language models in their professional contexts, underscoring the relevance of this study to contemporary professional practices. if the audio contains private conversations, any mentions of real names have been cut out beforehand, and this removal does not affect the main content of the meeting. ## results in this section, we analyzed objective and subjective results by combining the final study minutes, post-study questionnaires, and screen recordings captured during the process. the subjective ratings on minutes' quality and the ml gui heuristics are presented in figure 4 . ## conclusion and discussions meetalk addresses the challenges of long meeting minutes generation through innovative chunking and adaptive personalization. by performing asr on 30-second audio segments and labeling transcribed content for section allocation, meetalk enhances completeness and relevance, allowing users to review and modify labels in real-time. this process reduces input length for llms, improving the quality of summaries. additionally, the system's flexibility accommodates various data types and user preferences through rag and summary-based prompt engineering, enabling natural adaptation to user behavior. meetalk's design also includes an authenticity assessment mechanism that boosts user trust with feedback labels like "[not sure]." overall, meetalk's approach and principles can be generalized to other ai-driven applications beyond meeting note-taking, enhancing user engagement and facilitating multimodal processing tasks. in conclusion, this study introduces meetalk, an innovative adaptive ai system for personalized meeting minutes generation. by addressing key challenges in automated minute-taking, including effectively adapting to personal preferences, meetalk represents a significant advancement. the system's unique features, such as chapter allocation, chapter-wise writing, and adaptive learning from user modifications, offer a flexible and usercentric approach to generate meeting minutes. our comprehensive user study across diverse real-world scenarios demonstrates meetalk's effectiveness in producing high-quality, personalized minutes while enhancing user experience and trustworthiness. these findings validate meetalk's practical applicability, and further contribute valuable insights to the broader domain of personalized ai-assisted text processing and summarization. as organizations continue to rely heavily on meetings for information exchange and decision-making, systems like meetalk have the potential to significantly improve productivity and communication effectiveness. future research can build upon this foundation, further exploring the integration of adaptive personalization in various professional contexts and expanding the capabilities of ai-assisted documentation systems.