An Extractive Summarization of Multiple Documents Using Neural Network

Authors

  • Apurva Sawwalakhe  BE, Department of Computer Science and Engineering, Shrimati Rajshree Mulak College of Engineering, Nagpur, Maharashtra, India
  • Nikita Wanjari  BE, Department of Computer Science and Engineering, Shrimati Rajshree Mulak College of Engineering, Nagpur, Maharashtra, India
  • Shreya Paliwal  BE, Department of Computer Science and Engineering, Shrimati Rajshree Mulak College of Engineering, Nagpur, Maharashtra, India
  • Shubhangi Katare  BE, Department of Computer Science and Engineering, Shrimati Rajshree Mulak College of Engineering, Nagpur, Maharashtra, India
  • Vidhya Malve  BE, Department of Computer Science and Engineering, Shrimati Rajshree Mulak College of Engineering, 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 most prominent application for data pressure. Content rundown is a procedure of delivering a synopsis by lessening the measure of unique archive and relating vital data of unique report. There is emerging a need to give great outline in less time in light of the fact that in present time, the development of information increments hugely on World Wide Web or on client's desktops so Multi-Document rundown is the best apparatus for making synopsis in less time. This paper introduces a review of existing procedures with the oddities highlighting the need of smart Multi-Document summarizer.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2019-02-28

Issue

Volume 5, Issue 5, February-2019

Section

Research Articles

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

How to Cite

[1]
Apurva Sawwalakhe, Nikita Wanjari, Shreya Paliwal, Shubhangi Katare, Vidhya Malve, " An Extractive Summarization of Multiple Documents Using Neural Network, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 5, Issue 5, pp.56-63, February-2019.