Survey on Text Summarization Framework Using Machine Learning

Authors

  • Mrs. Kirti D Kulkarni  Department of Computer Engineering, Zeal College of Engineering and Research Pune, Maharashtra, India
  • Prof. Jareena N. Shaikh  Department of Computer Engineering, Zeal College of Engineering and Research Pune, Maharashtra, India

Keywords:

word vectors, word analogies, fast text, Integer linear programming, text summarising, natural language processing.

Abstract

An important natural language processing application, automatic text summarizing aims to condense a given textual content into a shorter model by using machine learning techniques. As media content transmission over the Internet continues to rise at an exponential rate, text summarization utilizing neural networks from asynchronous combinations of text is becoming increasingly necessary. Using the principles of natural language processing (NLP), this research proposes a framework for examining the intricate information included in multi-modal statistics and for improving the features of text summarization that are currently available. The underlying principle is to fill in the semantic gaps that exist between different types of content. In the following step, the summary for relevant information is generated using multi-modal topic modelling. Finally, all of the multi-modal aspects are taken into account in order to provide a textual summary that maximizes the relevance, non-redundancy, believability, and scope of the information by allocating an accumulation of submodular features.

References

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2022-05-07

Issue

Volume 9, Issue 3, May-June-2022

Section

Research Articles

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How to Cite

[1]
Mrs. Kirti D Kulkarni, Prof. Jareena N. Shaikh "Survey on Text Summarization Framework Using Machine Learning" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 9, Issue 3, pp.581-585, May-June-2022.