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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM28129

News Summarization of BBC Articles: A Multi-Category Approach

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM28129

Volume:

8 |

1 |

Jan 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM28129

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

SK

Sherilyn Kevin

AKP

Amit Kumar Pandey

BY

Bipin Yadav

GR

Gopal Rajbhar

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Abstract

In this research project, we explore the application of advanced natural language processing techniques to automatically summarize news articles from the BBC. The dataset comprises five distinct categories—business, entertainment, politics, sport, and tech—each containing a wealth of information. Our primary goal is to develop an efficient and accurate news summarization system using state-of-the-art language models. We employ the Hugging Face Transformers library to create a summarization pipeline capable of extracting key information from lengthy news articles.

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Cite This Article

Sherilyn Kevin, Amit Kumar Pandey, Bipin Yadav, Gopal Rajbhar, "News Summarization of BBC Articles: A Multi-Category Approach," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 1, Jan 2024. https://doi.org/10.55041/IJSREM28129

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12/31/45305

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Double-Blind Peer Review

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The authors declare no conflict of interest related to this research work.

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