Extractive and Abstractive Text Summarization Techniques
PL.Prabha1, M.Parvathy2

1PL.Prabha, Research Scholar,CSE Department, K.L.N. College of Information Technology, Pottapalyam, Sivagangai Dist.
2Dr. M.Parvathy, Professor , CSE Department, Sethu Institute of Technology, Pulloor, Kariapatti.

Manuscript received on April 02, 2020. | Revised Manuscript received on April 21, 2020. | Manuscript published on May 30, 2020. | PP: 1040-1044 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2235059120/2020©BEIESP | DOI: 10.35940/ijrte.A2235.059120
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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 generates an abstract version of information on a particular topic from various sources without modifying its originality. It is essential to dig information from the large repository of data, thereby eliminating the irrelevant information. The manual summarization consumes a large amount of time and hence an automated text summarization model is required. The summarization can be performed from a single source or multiple sources. The Natural Language Processing (NLP) based text summarization can be generally categorized as abstractive and extractive methods. The extractive methods mine the essential text from the document whereas the abstractive methods summarize the document by rewriting. The extractive summarization methods rely on topics and centrality of the document. The abstractive techniques transform the sentences based on the language resources available. This paper deals with the study of extractive as well as abstractive strategies in text summarization. Overall objective of this paper is to provide a significant direction to the researchers to learn about different strategies applied in text summarization. 
Keywords: Text summarization, Abstractive Summarization, Extractive Summarization, Natural Language Processing.
Scope of the Article: Natural Language Processing