Automatic Meeting Minutes Generation

In today’s fast-paced corporate and academic environments, efficient documentation of meetings is critical for

effective communication, decision tracking, and accountability. However, manual transcription and summarization of meeting

discussions are often time-consuming and error-prone. This paper proposes an automated system for generating meeting

minutes using Natural Language Processing (NLP) techniques. to enhance efficiency, accuracy, and accessibility. The system

processes audio or text transcripts of meetings and employs speech recognition, text summarization, and key information

extraction models to generate concise, coherent, and structured minutes. We explore a combination of extractive and abstractive

summarization methods, including transformer-based models like BERT and GPT, to capture salient discussion points, decisions

made, and action items. Additionally, named entity recognition (NER) and topic segmentation are used to enhance content

relevance and organization. Experimental