GUI Based Text Summarizing of Social Response
B. Hemanth Kumar1, L. Ramaparvathy2
1B. Hemanth Kumar*, UG Scholar, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India.
2Dr. L. Ramaparvathy, Assistant Professor, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India.
Manuscript received on January 12, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 1773-1776 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1710029420/2020©BEIESP | DOI: 10.35940/ijitee.D1710.029420
Open Access | Ethics and Policies | Cite | Mendeley
© 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 one of those utilizations of Natural Language Processing (NLP) which will undoubtedly hugy affect our lives. For the most part, Text outline can comprehensively be partitioned into two classifications, Extractive Summarization and Abstractive Summarization and the execution of seq2seq model for rundown of literary information utilizing of tensor stream/keras and showed on amazon or social reaction surveys, issues and news stories. Content rundown is a subdomain of Natural Language Processing that manages removing synopses from tremendous lumps of writings. There are two fundamental sorts of methods utilized for content rundown: NLP-based procedures and profound learning based strategies. Along these lines, our point is to look at spacy, gensim and nltk synopsis system by the info prerequisites. It will see a basic NLP-based system for content rundown. Or maybe it will basically utilize Python’s NLTK library for content abridging.
Keywords: Natural Language Processing, Sentence Ranking.
Scope of the Article: Natural Language Processing