AI Powered Multilingual Meeting Summarization

In current scenario, participants in online meetings face challenges in effectively capturing the vast amount of information exchanged during the sessions. Manual note-taking or reviewing lengthy meeting recordings can be time-consuming and prone to errors, leading to inefficiencies in information retrieval and follow-up actions. The goal of the project was to develop an automated system that can generate comprehensive summaries of meetings by analysing both textual and audio data. Leveraging advanced Natural Language Processing (NLP) and audio processing techniques, the model adeptly extracts key points, action items and relevant information from meeting transcripts and audio recordings eliminating the need for laborious manual review. The integration of Latent Semantic Analysis (LSA) algorithms bolsters the system's analytical capabilities, uncovering subtle semantic structures within the corpus of data. The automated system not only mitigates the risks of human error in manual summarization but also ensures consistency across varied meetings and participants, yielding reliable summaries. Additionally, the system demonstrates multilingual proficiency, facilitating seamless processing of meetings conducted in diverse languages. This paper delineates the comprehensive architecture, methodology, and performance evaluation of this automated meeting summarization system, marking a significant advancement in the stream of information retrieval and automated analysis of data.