[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123271-en":3,"doc-seo-123271-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123271,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",6,"Technology","Streamlining Information - Creating YouTube Video Summarizer Using Machine Learning","This study develops a user interface that enables retrieval of YouTube video summaries by integrating Natural Language Processing and Machine Learning. With millions of videos uploaded daily, finding relevant information becomes slow, and search results often fail to surface meaningful content. The proposed system uses an abstractive summarization model: it extracts transcripts from YouTube videos and produces condensed summaries. The approach reduces time spent consuming content while preserving key information for better decision-making. Implementation is still ongoing, and the paper presents the conceptual framework and preliminary findings.","Streamlining Information: Creating YouTube Video Summarizer Using Machine Learning  \nAbhash Srivastava 1, Bramah Hazela2, Shikha Singh3, Vineet Singh4  \n1,2,3,4Amity School of Engineering and Technology, Amity University, Uttar Pradesh, Lucknow, India  \n[1](1abhash.srivastava@s.amity.edu)[abhash.srivastava@s.amity.edu](1abhash.srivastava@s.amity.edu), [2](2bhazela@lko.amity.edu)[bhazela@lko.amity.edu](2bhazela@lko.amity.edu), [3](3ssingh8@lko.amity.edu)[ssingh8@lko.amity.edu](3ssingh8@lko.amity.edu), [4](4vsingh@lko.amity.edu)[vsingh@lko.amity.edu](4vsingh@lko.amity.edu)  \n\n| How to cite this paper: A. Srivastava, B. Hazela, S. Singh, V. Singh,“Streamlining Information: Creating YouTube Video Summarizer using Machine Learning,” Journal of Management and Service Science (JMSS), Vol. 04, Iss. 02, S. No. 063, pp. 1-9, 2024.\u003Cbr>[https://doi.org/10.54060/a2zjourna](https://doi.org/10.54060/a2zjourna) |\n| --- |\n| ls.jmss.63\u003Cbr>Received: 04/09/2023\u003Cbr>Accepted: 20/05/2024\u003Cbr>Online First: 10/06/2024\u003Cbr>Published: 25/11/2024\u003Cbr>Copyright © 2024 The Author(s) . This work is licensed under the Creative Commons Attribution International License (CC BY 4.0) . [http://creativecommons.org/licens](http://creativecommons.org/licens) |\n| es/by/4 .0/\u003Cbr>  Open Access  |\n\nAbstract  \nThe aim of this study is to develop a user interface facilitating the retrieval of YouTube video summaries through the integration of Natural Language Processing (NLP) and Machine Learning techniques. With the continuous influx of videos uploaded to YouTube on a daily basis, locating relevant content has become increasingly challenging. Often, significant time and effort are expended in searching for desired content, with outcomes often proving futile due to the inability to extract meaningful information. Our project addresses this issue by providing a solution that efficiently summarizes videos, presenting users with concise yet comprehensive insights. Utilizing an abstractive summarization model, the system extracts transcripts from YouTube videos and generates condensed summaries, effectively reducing the time required for content consumption while preserving crucial information. While the implementation phase is still in progress, this paper presents the conceptual framework and initial findings ofour research endeavor.  \nKeywords  \nNatural Language Processing, Machine Learning, Abstractive summarization complexity  \n1. Introduction  \nIn the contemporary era of digitalization, technology plays a pivotal role in shaping societal advancement. The proliferation  \nof internet users, accessing online platforms round the clock, has resulted in a surge of information consumption. However, amidst this wealth of data, retrieving relevant information has become increasingly challenging and time-consuming. YouTube, as a premier platform for content dissemination, offers content creators unparalleled reach, leading to an exponential rise in the volume of available content. With approximately 3.7 million videos uploaded daily, navigating through this vast repository to find pertinent content has become akin to searching for a needle in a haystack.  \nMoreover, this abundance of content has given rise to the proliferation of clickbait videos, further complicating the search process and often leaving users with minimal or irrelevant information. To address this challenge, summarization techniques offer a promising solution. Summarization enables users to distil key concepts from text or video content efficiently, facilitating the identification of essential information while filtering out extraneous material. Additionally, by providing language conversion options, users with diverse linguistic preferences can access summarized content more effectively.  \nA brief overview of the video's content provided by the summary empowers users to make informed decisions about investing their time and attention. This paper focuses on video summarization utilizing the abstractive method, whi","cbCailCfGWhj7EGA","https://ap.wps.com/l/cbCailCfGWhj7EGA","pdf",399850,1,9,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n# Literature Survey","[{\"question\":\"What problem does the YouTube video summarizer address?\",\"answer\":\"It addresses the difficulty of finding relevant information among the large volume of daily YouTube uploads, where searching can waste time and yield unhelpful results.\"},{\"question\":\"How does the system generate video summaries?\",\"answer\":\"It extracts video transcripts from YouTube and uses an abstractive summarization model to generate condensed summaries that preserve crucial information.\"},{\"question\":\"Why does the paper focus on abstractive summarization rather than extractive summarization?\",\"answer\":\"Abstractive summarization aims to produce coherent summaries by analyzing the text, rather than selecting portions verbatim as in extractive approaches.\"}]","Streamlining Information - 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