Extractive Multi-Document Summarization using Neural Network

Authors

  • Ravina Mohod  M.Tech Student, Department of Computer Science and Engineering, Gurunanak Institute of Engineering and Technology, Nagpur, Maharashtra, India
  • Prof. Vijaya Kamble  Assistant Professor, Department of Computer Science and Engineering, Gurunanak Institute of Engineering and Technology, Nagpur, Maharashtra, India

Keywords:

Multi-Document Summarization; Clustering Based; Extractive and Abstractive approach; Ranked Based; LDA Based; Natural Language Processing

Abstract

Natural language processing gives Text Summarization, which is the unmistakable application for information weight. Content diagram is a course of action of passing on a rundown by reducing the measure of outstanding document and relating essential information of champion report. There is rising a need to give grand chart in less time in light of the way that in exhibit time, the progress of data increments immensely on World Wide Web or on customer's work zones so Multi-Document once-completed is the best mechanical social affair to impact plot in less to time. This paper demonstrates an audit of existing techniques with the erraticism’s including the need of sharp Multi-Document summarizer.

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International Journal of Scientific Research in Science and Technology

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Published

2018-06-30

Issue

Volume 4, Issue 8, May-June-2018

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Ravina Mohod, Prof. Vijaya Kamble, " Extractive Multi-Document Summarization using Neural Network, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 4, Issue 8, pp.173-178, May-June-2018.