Extractive Text Summarization using Deep Natural Language Fuzzy Processing
Neelima G1, Veeramanickam M.R.M2, Sergey Gorbachev3, Sandip A. Kale4

1Neelima G, Vignan’s Institute of Information Technology, Visakhapatnam, Andhra Pradesh, India.

2Veeramanickam M.R.M, Vignan’s Institute of Information Technology, Visakhapatnam, Andhra Pradesh, India.

3Sergey Gorbachev, Сandidate of Technical Sciences, National research Tomsk State University, Russia.

4Sandip A. Kale, Trinity College of Engineering and Research, Savitribai Phule Pune University, Pune, India.

Manuscript received on 10 April 2019 | Revised Manuscript received on 17 April 2019 | Manuscript Published on 26 July 2019 | PP: 990-993 | Volume-8 Issue-6S4 April 2019 | Retrieval Number: F12030486S419/19©BEIESP | DOI: 10.35940/ijitee.F1203.0486S419

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Text summarization is most trending research areas in a modern context. The main aim of this project is to reduce text size while preserving the information underlying into it. In summary construction level, in general, given complex task which are basically will involve with deep natural language fuzzy processing methodologies. In general, an extractive based summary method is the very simple original text of subset of which will not guarantee as best narrative coherence output, because they are most conveniently representing an approximate summarized content from given text-based only on relevance judgment. In an automatic process of fuzzy summarization which is divided into the following steps: Pre-processing (sentence segmentation, tokenization, stop words removal), Feature Extraction, Sentence Scoring, Sentence Ranking and Summary Extraction.

Keywords: Natural Language Fuzzy Processing, Text Summarization, Tokenization, Naive-Bayes.
Scope of the Article: Fuzzy Logics