Application of Different Algorithms to Optimize Abstractive Summarization
Bhumika1, V. Srikanth2
1Bhumika, Assistant Professor, Department of Computer Applications, Dr. Y.S. Parmar Govt. PG College Nahan, District Sirmaur, Himachal Pradesh, India.
2Dr. V. Srikanth, Associate Professor, Department of AIT IBM CSE Chandigarh University, Panjab, India.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 664-668 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6146018520/2020©BEIESP | DOI: 10.35940/ijrte.E6146.018520
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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: Summarization is used to extract most relevant content from a huge content. It can be extractive and abstractive. But different people may have different scope of relevance in different area. Content can be in the form of opinions, news, judgments and ideas etc. Extractive Summarization extracts most focusing content without any change in the original content. Abstractive summarization is a knowledgebase extraction with some modification in original content. In this paper first we discuss various algorithms for abstractive summarization then on the basis of merits of various algorithms we discuss, how various algorithm may help to optimize Abstractive Summarization.
Keywords: Abstractive Summarization, Diverse Decoding [6], Opinion Summarization.
Scope of the Article: Network Based Applications