AI-Driven Meeting Transcriber & Summarizer: An Intelligent System for Automated Meeting Documentation

Abstract

This paper presents the design and implementation of an AI-Powered Meeting Transcription & Summarization Tool that leverages advanced natural language processing (NLP) and machine learning (ML) technologies to automatically convert meeting audio recordings into structured, actionable business documents. The system addresses the critical business need for efficient meeting documentation by combining speech-to-text conversion, intelligent content analysis, and automated summarization capabilities. The proposed solution integrates multiple cutting-edge technologies including Large Language Models (LLMs), NLP algorithms, and cloud-based APIs to create a comprehensive meeting management workflow. The system extracts key discussion points, action items, decisions, and follow-up tasks from meeting transcripts while providing seamless integration with popular workplace collaboration tools such as Notion, Monday.com, and Slack. Key contributions include the development of a context-aware NLP model specifically fine-tuned for meeting scenarios, implementation of intelligent categorization algorithms for meeting content, and creation of a scalable web-based platform supporting real-time processing and multi-format output generation. Evaluation demonstrates significant improvements in meeting documentation efficiency while maintaining high accuracy in information extraction and summarization quality.