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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | youtube transcript summarization using TF-IDF algorithm |
| Authors Name: | Varun N |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_196538
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| Published Paper Id: | IJNRD2306300 |
| Published In: | Volume 8 Issue 6, June-2023 |
| DOI: | |
| Abstract: | YouTube is a massive platform that hosts a vast amount of video content. However, finding relevant information from these videos can be time-consuming and challenging, especially when one wants to understand the key points of a video quickly. This problem can be addressed by automatically summarizing the transcript of a video into a concise and informative summary. The TF-IDF (Term Frequency-Inverse Document Frequency) algorithm is used to summarize the transcript of YouTube videos. The TF-IDF algorithm is a popular information retrieval technique that measures the importance of a word in a document. The algorithm calculates the term frequency (TF) of each word in a transcript and measures the inverse document frequency (IDF) of the words across a large corpus of documents. The TF-IDF algorithm is applied to the transcript of each video to determine the most important words in the transcript. The summary is then generated by selecting a subset of the most important sentences that contain these important words. The generated summary effectively condenses the transcript into a concise and informative summary that can be quickly consumed. The results of the project indicate that the TF-IDF algorithm is an effective approach for summarizing the transcript of YouTube videos. The generated summaries accurately capture the key points of the video, making it easier for viewers to quickly understand the content of the video. This approach can be useful for a variety of applications, including content discovery, information retrieval, and education. |
| Keywords: | Text Frequency – Inverse document, accuracy, frequency – Text ranking, - Summarization, Natural Language Processing, Extractive, Youtube. |
| Cite Article: | "youtube transcript summarization using TF-IDF algorithm", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 6, page no.c872-c877, June-2023, Available :http://www.ijnrd.org/papers/IJNRD2306300.pdf |
| Downloads: | 000118756 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2306300 Registration ID: 196538 Published In: Volume 8 Issue 6, June-2023 DOI (Digital Object Identifier): Page No: c872-c877 Country: Tumakuru, Karnataka, India Research Area: Information Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2306300 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2306300 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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