AI Summarizer: Interactive Multi-Modal Processing for Lectures, Meetings and Text Documents

Author(s)	Mr. Rahul Rajendra Dhamdhere, Mr. Manthan Vijayrao Dhawale, Mr. Satyajeet Ramesh Jagtap, Mr. Harsh Gokul Memane, Mr. Shashank Vikaram Lahane, Ms. Sneha Salvekar
Country	India
Abstract	This paper introduces an AI-powered summarization system that processes both text and audio content—such as lectures and meetings—to improve productivity. It integrates OpenAI Whisper for transcription, Nomic embeddings for extractive summarization, and DeepSeek’s language model (via Ollama) for generating refined summaries and enabling chatbot interaction. The system runs locally using a Flask backend and HTML/JavaScript frontend. Whisper achieves a Word Error Rate (WER) of ~10%, and the system’s summarization accuracy averages 77.46%, as evaluated by Grok. Designed for students and professionals, future enhancements will include real-time processing and optional cloud integration with privacy safeguards.
Keywords	Text summarization, Extractive summarization, abstractive summarization, Context Vector, Transformers, Ollama, Flask, Nomic-Embed Text
Field	Engineering
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-06-09
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.47071
Short DOI	https://doi.org/g9pzvq

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International Journal For Multidisciplinary Research