youtube transcript summarization using TF-IDF algorithm

Registration ID: IJNRD_196538

Published ID: IJNRD2306300

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Varun N

Text Frequency – Inverse document, accuracy, frequency – Text ranking, - Summarization, Natural Language Processing, Extractive, Youtube.

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.

Published Paper   E-Certificate

Varun N (June-2023). youtube transcript summarization using TF-IDF algorithm. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(6), c872-c877. https://ijnrd.org/papers/IJNRD2306300.pdf

Volume 8 Issue 6, June-2023

Pages : c872-c877

Paper Reg. ID: IJNRD_196538

Published Paper Id: IJNRD2306300

Downloads: 000122254

Research Area: Information Technology 

Author Type: Indian Author

Country: Tumakuru, Karnataka, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2306300.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2306300

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