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Text Summarization Framework Using Advanced Machine Learning Algorithms

EasyChair Preprint no. 2881

4 pagesDate: March 6, 2020


In recent years, much work has been performed to summarize meeting recordings, sport videos, movies, pictorial storylines and social multimedia. Automatic text summarization is an essential natural language processing (NLP) application that goals to summarize a given textual content into a shorter model. The quick development in media data transmission over the Internet requests content outline utilizing neural system from nonconcurrent blend of content. This paper speaks to a structure that uses the methods of NLP strategy to analyze the elaborative data contained in multi-modular insights and to improve the parts of content rundown. The essential idea is to connect the semantic holes among content substance. After, the created outline for significant data through multi-modular subject demonstrating. At long last, all the multi-modular components are considered to create a literary outline by expanding the significance, non-excess, believability and degree through the assigned collection of submodular highlights. The exploratory outcome shows that Text Summarization system outflanks other serious strategies.

Keyphrases: feature selection, machine learning, Sentence Embedding, Summarization

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Pallavi Kohakade and Sujata Jadhav},
  title = {Text Summarization Framework Using Advanced Machine Learning Algorithms},
  howpublished = {EasyChair Preprint no. 2881},

  year = {EasyChair, 2020}}
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