A Unified Video Content Understanding Framework for Youtube and Local Videos with Multilingual Summarization Support

Video has become one of the most prevalent methods of information dissemination and learning in today's digital world. However, manually viewing long videos to extract key points is very time-consuming and hence inefficient. In this project, we plan to develop a Video-to-Text Summarizer that automatically converts video content-whether from YouTube links or local files-into concise, meaningful summaries. The system will extract the audio from the video and use an Automatic Speech Recognition model to produce text. Further, using appropriate Natural Language Processing (NLP) techniques in conjunction with the state-of-the-art summarization algorithms-like transformer-based models such as BERT or GPT-the obtained transcript will be summarized. It thus helps users comprehend the core content of lengthy videos without having to go through every second of those videos. The proposed system can be applied in various domains like education, research, media analysis, and corporate training by making the consumption of content highly effective and accessible.