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1705416PublishedVol 7 · Issue 7

News Summarization Articles by Using NLP

Amit Kumar Pandey Pradeep Tripathi

Subject area: Science,Engineering and Technology  ·  Area of research: Machine Learning, Deep Learning, NLP

Abstract

In this research endeavor, we delve into the realm of news summarization, harnessing the power of advanced natural language processing techniques for the automated condensation of BBC articles. Our dataset encompasses five diverse categories?business, entertainment, politics, sport, and tech?offering a rich tapestry of information. The central objective of our study is to craft a news summarization system that is both efficient and accurate, leveraging cutting-edge language models. We utilize the Hugging Face Transformers library to construct a summarization pipeline adept at distilling crucial insights from extensive news articles.

How to cite this paper

Amit Kumar Pandey, Pradeep Tripathi "News Summarization Articles by Using NLP" Iconic Research And Engineering Journals Volume 7 Issue 7 2024 Page 339-343
Amit Kumar Pandey, Pradeep Tripathi "News Summarization Articles by Using NLP" Iconic Research And Engineering Journals, vol. 7, no. 7, Jan. 2024
Amit Kumar Pandey, Pradeep Tripathi (2024). News Summarization Articles by Using NLP. Iconic Research And Engineering Journals, 7(7).
Amit Kumar Pandey, Pradeep Tripathi "News Summarization Articles by Using NLP" Iconic Research And Engineering Journals, vol. 7, no. 7, Jan. 2024.
@article{1705416,
      author = {Amit Kumar Pandey, Pradeep Tripathi},
      title = {News Summarization Articles by Using NLP},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {7},
      pages = {339-343},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705416.pdf},
      abstract = {In this research endeavor, we delve into the realm of news summarization, harnessing the power of advanced natural language processing techniques for the automated condensation of BBC articles. Our dataset encompasses five diverse categories?business, entertainment, politics, sport, and tech?offering a rich tapestry of information. The central objective of our study is to craft a news summarization system that is both efficient and accurate, leveraging cutting-edge language models. We utilize the Hugging Face Transformers library to construct a summarization pipeline adept at distilling crucial insights from extensive news articles.},
      month = {January},
  }