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This paper explores the evolving domain of YouTube video summarization, focusing on the crucial role of Natural Language Processing (NLP) techniques in addressing the challenges raised by the vast and continually expanding environment of online multimedia content. The paper involves a complete perspective on the necessity for automated video summarization tools in managing the overpowering abundance of YouTube videos. It researches the application of NLP methods to extract meaningful textual information from online multimedia content. By combining the concept of NLP and video summarization, this paper provides researchers with a deep understanding of how NLP increases efficiency, accuracy, and user experience in accessing YouTube videos. This paper introduces an automatic video summarization technique that provides the feature of multilingual which is absent in most of the video summarization tools. This tool allows users to select their desired language for summarized text and audio conversion which improves the experience of the user. The tool separates sentences into positive, negative, and neutral categories which provides user with a complete understanding of the sentimental tone of the video. This paper provides understanding of combination of NLP, multilingual conversion of summary, and sentiment analysis which provides the best combination for automatic video summarization. This combination of NLP, multilingual conversion, and sentiment analysis provides an efficient way to utilize the YouTube video content.

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An Extensive Machine Learning-Based Tool Development for Automatic Transcription and Contextual Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • Vishal kumar,
  • Ritwik Arora,
  • Raunit Arora,
  • Sonakshi Vij

摘要

This paper explores the evolving domain of YouTube video summarization, focusing on the crucial role of Natural Language Processing (NLP) techniques in addressing the challenges raised by the vast and continually expanding environment of online multimedia content. The paper involves a complete perspective on the necessity for automated video summarization tools in managing the overpowering abundance of YouTube videos. It researches the application of NLP methods to extract meaningful textual information from online multimedia content. By combining the concept of NLP and video summarization, this paper provides researchers with a deep understanding of how NLP increases efficiency, accuracy, and user experience in accessing YouTube videos. This paper introduces an automatic video summarization technique that provides the feature of multilingual which is absent in most of the video summarization tools. This tool allows users to select their desired language for summarized text and audio conversion which improves the experience of the user. The tool separates sentences into positive, negative, and neutral categories which provides user with a complete understanding of the sentimental tone of the video. This paper provides understanding of combination of NLP, multilingual conversion of summary, and sentiment analysis which provides the best combination for automatic video summarization. This combination of NLP, multilingual conversion, and sentiment analysis provides an efficient way to utilize the YouTube video content.