Meeting Summarizer Using Natural Language Processing

Abstract

Abstract: Meeting transcripts produced by tools like Microsoft Teams and Google Meet, are useful for recording discussions and decisions made during meetings. However, reading through long transcripts can be time-consuming and may not always be the most efficient way to understand the key points and conclusions of a meeting. Meeting summarization is a subfield of natural language processing that can extract important information from meeting transcripts and generate a concise summary. This summary can be used to quickly understand the key points and conclusions of the meeting, and can be especially useful for stakeholders who were not able to attend the meeting in person. Several natural language processing techniques can be used to create summaries of meeting transcripts, such as the term frequency-inverse document frequency (TF-IDF) method, PageRank algorithm, Named Entity Recognition, Topic Modeling and specific summarization algorithms. Each technique has its own advantages and limitations, and the appropriate technique can be chosen based on the specific needs and requirements of the organization, such as accuracy, efficiency, and customization.