Extractive Text Summarization for Sports Articles using Statistical Method
Sai Teja Polisetty1, K Selvani Deepthi2, Shaik Ameen3, Ravivarma G4, M Mounisha5
1Sai Teja Polisetty*, pursuing B. Tech final year, Department of CSE, Anil Neerukonda Institute of Technology and Sciences, India.
2Dr. K. Selvani Deepthi, Associate Professor, Department of CSE, Anil Neerukonda Institute of Technology and Sciences, India.
3Shaik Ameen, pursuing B. Tech final year, Department of CSE, Anil Neerukonda Institute of Technology and Sciences, India.
4Ravivarma G, pursuing B. Tech final year, Department of CSE, Anil Neerukonda Institute of Technology and Sciences, India.
5M Mounisha, pursuing B. Tech final year, Department of CSE, Anil Neerukonda Institute of Technology and Sciences, India.
Manuscript received on February 28, 2020. | Revised Manuscript received on March 22, 2020. | Manuscript published on March 30, 2020. | PP: 5622-5627 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9965038620/2020©BEIESP | DOI: 10.35940/ijrte.F9965.038620
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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: The past decade has endorsed a great rise in Artificial Intelligence. Text summarization which comes under AI has been an important research area that identifies the relevant sentences from a piece of text. By Text Summarization, we can get short and precise information by preserving the contents of the text. This paper presents an approach for generating a short and precise extractive summary for the given document of text. A statistical method for extractive text summarization of sports articles using extraction of various features is discussed in this paper. The features taken are TF-ISF, Sentence Length, Sentence Position, Sentence to Sentence cohesion, Proper noun, Pronoun. Each sentence is given a score known as the predictive score is calculated and the summary for the given document of text is given based on the predictive score or also known as the rank of the sentence. The accuracy is checked using the BBC Sports Article dataset and sports articles of various newspapers like the New York Times, CNN. The precision of 73% is acquired when compared with System Generated Summary (SGS) and manual summary, on an average.
Keywords: Artificial Intelligence, Cosine similarity, Natural Language Processing, System Generated Summary (SGS), Term frequency inverse sentence frequency.
Scope of the Article: Artificial Intelligence and Machine Learning.