Streamlining Information: Creating YouTube Video Summarizer Using Machine Learning
DOI:
https://doi.org/10.54060/a2zjournals.jmss.63Keywords:
Natural Language Processing , Machine Learning , Abstractive summarization complexityAbstract
The 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 of our research endeavor.
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Copyright (c) 2021 Abhash Srivastava, Dr. Bramah Hazela, Dr. Shikha Singh, Vineet Singh
This work is licensed under a Creative Commons Attribution 4.0 International License.
