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Research Paper | Computer Engineering | Volume 12 Issue 6, June 2023 | Pages: 913 - 918 | India
Graph Based Abstractive Text Summarization of YouTube Comments
Abstract: Summarizing documents or reviews has received a lot of attention in recent years due to the explosive expansion of online documents having large sizes and shopping websites having a large number of reviews. In this fast-paced environment, people require things to be done fast, saving time. Reviews and documents are being created in enormous quantities every day. Understanding the context of document/ reviews and converting them into a specific format, like a summary to conserve storage space and aid in information acquisition in a short period of time. We test a hybrid end-to-end model solution that synthesizes input video comments and abstract text summaries using natural language processing and graph-based methods. This methodology first provides an extractive text summary of the comments. We propose a joint end-to-end model using the FP-Growth method and T5 Model for generating an abstractive text summary of the video input provided.
Keywords: Machine learning, Deep learning, Natural Language Processing, Extractive & Abstractive Summary
How to Cite?: Vijayendra S. Gaikwad, "Graph Based Abstractive Text Summarization of YouTube Comments", Volume 12 Issue 6, June 2023, International Journal of Science and Research (IJSR), Pages: 913-918, https://www.ijsr.net/getabstract.php?paperid=SR23604110517, DOI: https://dx.doi.org/10.21275/SR23604110517