Open AccessExtractive Text Summarization using Deep Natural Language Fuzzy Processing
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.f1203.0486s419
Subject(s) - automatic summarization , computer science , natural language processing , artificial intelligence , sentence , lexical analysis , text graph , context (archaeology) , natural language , relevance (law) , feature (linguistics) , coherence (philosophical gambling strategy) , information retrieval , linguistics , mathematics , paleontology , philosophy , statistics , political science , law , biology
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.