A Retrieval-Augmented Framework For Meeting Insight Extraction

Meetings are vital for collaboration and decision-making in professional environments, yet recalling key details from past discussions can be challenging and this impacts productivity. In this paper, we address this issue by developing a solution that extracts crucial insights from historical meeting records using Retrieval Augmented Generation (RAG) techniques. Users can easily upload meeting records and query for relevant information. A core feature of our proposed system is grouping meetings based on abstractive summaries, using state-of-the-art clustering algorithms extensively trained for accuracy. Upon user inquiry, the system identifies the most relevant cluster and retrieves related conversations from the Pinecone vector store database. These conversations, paired with custom prompts, are processed through a Large Language Model (LLM) to generate precise responses. Our optimization efforts focus on exploring various encoders and LLMs, with fine-tuning to ensure seamless integration and high performance. This approach tackles challenges in meeting summarization, content discovery, and user-friendly information retrieval.