Youtube Transcript Summarizer

This paper presents the development of an innovative video summarization system utilizing Natural Language Processing (NLP) and Machine Learning techniques. The exponential growth of video content on platforms like YouTube has posed a significant challenge in efficient content consumption. To address this challenge, we propose a YouTube transcript summarizer that generates concise and informative summaries of video transcripts, enabling users to grasp key content material without the need for complete viewing. Unlike images, where data extraction is feasible from a single frame, understanding the context of a video typically requires viewing the entire content. Our study seeks to alleviate this issue by reducing the transcript length while preserving its comprehensiveness. Leveraging NLP and Machine Learning algorithms, such as Logistic Regression, we extract transcripts from user-provided video links and summarize the content into a precise representation using Word2Vec and Logistic Regression. By distilling the essence of YouTube video transcripts while retaining their pivotal elements, our system offers users a more streamlined and effective way to consume video content.