Scalable Summarization of Long-Form Video Transcripts Using NLP

This research designs a system that, with the help of NLP, would sum up the YouTube video transcripts properly without losing any important information. Day in day out, time keeps on recording increasing numbers of videos that are housed in internet-based platforms. Educating is the most prominent explanation for all the global content being available and accessible. One might also train on the different networks such as Google, Facebook, Instagram, YouTube, and once again Google. The underlying truth that the viewer has to watch the entire film to really understand and appreciate the broader perspective shows one key element-is comparable with images where so much information and data can be extracted and understood from just one frame; it indicates that data extraction from videos is a both an important and challenging question. Thus, the aim of this research project is to comfortably shorten and summarize the length of the video transcript so that it can easily be understood. The proposed approach is to get transcripts by using the link given from the video provided by the user and then use TD-IDF combined with the BART transformer in order to text summarize. The model developed here successfully satisfies the consideration of video URLs shared by the user and the exact summary duration needed. At the end, this results in the detailed summary of the transcript for the videos. Comparing with the other proposed methods put forward, the final text translation has been found to be present much earlier than what the results initially suggested. Moreover, a significant point has to be added that the final text absolutely depicts the theme of the movie without any kind of change or alteration.