Document Summarization for News Articles and Fake News Detection

@article{1704515,
      author = {Shreya U Chaudhary, Shashwat Tandon, Aniket Fand, Atharva Kadilkar, Urmila Pawar},
      title = {Document Summarization for News Articles and Fake News Detection},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {11},
      pages = {845-851},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704515.pdf},
      abstract = {The recent surge of social media groups, forums, and pages with people from all walks of life sharing information about all worldly events ranging from festivals to global food crises. Purportedly, a lot of these blocks of information tend to be fabricated owing to reasons as simple as humor. In a country as big as ours where misinformation can cause mayhem of unprecedented scale. To prevent any such mishappening we aim to define a model which can study, learn, and then classify any such news as real or fake. Also, to give the user a more concise representation we seek to design a summarization model which will study the given news and present a distilled version with only the most relevant information intact.},
      keywords = {Naive Bayes Theorem, k-means clustering, decision tree, support vector machine, social media, Artificial Intelligence, etc.},
      month = {May},
  }