Auto Text Summarization

@article{1702654,
      author = {Aditya Kirtane, Akhil Pawar, Suhas Tambe, Prof. M. R. Gorbal},
      title = {Auto Text Summarization},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {4},
      number = {10},
      pages = {76-83},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1702654.pdf},
      abstract = {Automatic text summarization is basically summarizing of the given paragraph using natural language processing and machine learning. There has been an explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively y summarized to be useful.  In this review, the main approaches to automatic text hello.  We review the different processes for summarization and    describe the effectivenessand shortcomings of the different methods.Two types   will be used i.e.-extractive approach and abstractive approach. Thebasic idea behind summarization is finding the subset of the data which contains the information of all the set. There is a great need to reduce unnecessary data. It is very difficult to summarize the document manually so there is the great need of automatic method. The extractive approach basically chooses the various and unique sentences, sections and so forth make a shorter type of the first report. The sentences are estimated and chosen based on accurate highlights of the sentences. In the Extractive technique, we have to choose the subset from the given expression or sentences in given frame of the synopsis.},
      keywords = {Auto Text; Extractive; Summarization},
      month = {April},
  }