Query Based Text Summarization
Sindhura Mangalampati1, Raveendra Babu Ponnuru2
1Sindhura Mangalampagti, Department of Computer Science and Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India.
2Raveendra Babu Ponnuru, Department of Computer Science and Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India.
Manuscript received on 03 March 2019 | Revised Manuscript received on 08 March 2019 | Manuscript published on 30 July 2019 | PP: 1475-1477 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2113078219/19©BEIESP | DOI: 10.35940/ijrte.B2113.078219
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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: Now a day’s, The advancement of technology implies the large amount of online textual Information, this leads to a need for Text Summarizers which can provide important textual data from large data source like www, into a well-structured document formats. It becomes an alive analysis activity to specify the related data that is retrieved from huge set of documents which are established from various sectors like social media. Text summary is the action of precise the documents by protecting the key concepts of the text. This process can be accomplished via extractive summary and abstractive summary. In this paper, we suggested summarization technique that is to be applied on online information depending upon the User required Document.
Index Terms: Abstractive Summary, Extractive Summary, Information Retrieval, Text Summarizer.
Scope of the Article: Information Retrieval