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Text Summarization Framework Using Machine Learning

EasyChair Preprint no. 2426

4 pagesDate: January 20, 2020


Automatic text summarization is an essential naturallanguage processing application that goals to summarize agiven textual content into a shorter model. The fast growthin media information transmission over the Internet demandstext summarization using neural network from asynchronouscombination of text. This paper represents a framework thatutilizes the techniques of NLP technique to examine the elab-orative information contained in multi-modal statistics and toenhance the aspects of text summarization. The basic conceptis to bridge the semantic gaps among text content. After, thegenerated summary for important information through multi-modal topic modeling. Finally, all the multi-modal factors areconsidered to generate a textual summary by maximizing theimportance, non-redundancy, credibility and scope through theallocated accumulation of submodular features. The experimentalresult shows that Text Summarization framework outperformsother competitive techniques.

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 Machine Learning},
  howpublished = {EasyChair Preprint no. 2426},

  year = {EasyChair, 2020}}
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