News Summarization Articles by Using NLP

References

[1] P. Sethi, S. Sonawane, S. Khanwalker and R. B. Keskar, "Automatic text summarization of news articles," 2017 International Conference on Big Data, IoT and Data Science (BID), Pune, India, 2017, pp. 23-29, doi: 10.1109/BID.2017.8336568.

[2] Y. Du and H. Huo, "News Text Summarization Based on Multi-Feature and Fuzzy Logic," in IEEE Access, vol. 8, pp. 140261-140272, 2020, doi: 10.1109/ACCESS.2020.3007763.

[3] Yang Y, Tan Y, Min J, Huang Z. Automatic text summarization for government news reports based on multiple features. The Journal of Supercomputing. 2023 Aug 30:1-7.

[4] Esmaeilzadeh S, Peh GX, Xu A. Neural abstractive text summarization and fake news detection. arXiv preprint arXiv:1904.00788. 2019 Mar 24.

[5] Y. Chen and Q. Song, "News Text Summarization Method based on BART-TextRank Model," 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China, 2021, pp. 2005-2010, doi: 10.1109/IAEAC50856.2021.9390683.

[6] T. B. Mirani and S. Sasi, "Two-level text summarization from online news sources with sentiment analysis," 2017 International Conference on Networks & Advances in Computational Technologies (NetACT), Thiruvananthapuram, India, 2017, pp. 19-24, doi: 10.1109/NETACT.2017.8076735.

[7] J. Cheng, F. Zhang and X. Guo, "A Syntax-Augmented and Headline-Aware Neural Text Summarization Method," in IEEE Access, vol. 8, pp. 218360-218371, 2020, doi: 10.1109/ACCESS.2020.3042886.

@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},
  }