![]() In abstractive summarization, the system will generate a summary using its own terms depending on the input content. The system will extract the important paragraphs and contents from the given passage and combine these extracted paragraphs to construct the summarised text in extractive summarization. In NLP, there are two kinds of summarising: extractive summarization and abstractive summarization. Summarization is the process of creating succinct, intelligible notes for a given lengthy text document without omitting the passage’s main information. Let’s go over the fundamentals of the NLP study.įirst and foremost, let us define summarization. This will be beneficial for quickly obtaining a summary of various course videos. Because the major material of many videos is only 50–60% of the entire duration, our YouTube summarizer will condense the content of the video by maintaining all the vital parts and making it brief and easy to understand. ![]() In this article, we will look at a small NLP project called a YouTube Summarizer, which will summarise the content (subtitle) of a YouTube video. Have you ever imagined having a short summary of a large YouTube tutorial or video for rapid reading before watching the video? This will undoubtedly save you a lot of time by providing you with a quick understanding or summarization of the video in a short period of time. Text summarization, chatbots, machine translation, text generation, and other applications of NLP are popular right now. NLP is used in a variety of applications, ranging from message spam filtering to medical diagnosis via a chatbot. Natural Language Processing, or NLP, is one of the most rapidly growing fields right now.
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