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Abstract: In an era characterized by an incessant influx of information, the demand for efficient knowledge extraction tools has become paramount. This research introduces a Meeting Sum- marizer, a cutting-edge system that amalgamates speech recog- nition and natural language processing (NLP) to autonomously distill salient information from recorded meetings. The primary objective of this research is to alleviate the cumbersome process of manual meeting summarization by harnessing the capabilities of advanced machine learning techniques tailored to audio data. The Meeting Summarizer leverages state-of-theart speech recognition algorithms to transcribe spoken content into textual form, laying the foundation for subsequent NLPbased analysis. Through the integration of deep learning methodologies, the system discerns key discussions, identifies critical components, and extracts context from meeting transcripts. The synergy of speech recognition and NLP empowers the system to comprehend linguistic nuances, enabling it to adapt to diverse meeting contexts.
Manas Bidkar (Fri,) studied this question.