Video Summarization Tool Using Machine Learning

Abstract

Video summarization is a crucial task in multimedia processing, allowing efficient content consumption by extracting the most relevant parts of a video. This research focuses on an automated video summarization system using machine learning techniques. The system integrates video processing, speech-to-text transcription, summarization, and translation. MoviePy is used for video extraction, Whisper for transcription, and the Hugging Face Transformers pipeline for text summarization. Google Trans is employed for multilingual support. The backend is developed using Django, while MongoDB serves as the database. This paper explores the methodology, implementation, and evaluation of the system, demonstrating its effectiveness in summarizing lengthy videos into concise textual representations.