ACTSMLT: Automatic Classification of Text Summarization using Machine Learning Technique
Ramya R S1, Darshan M2, Sejal D3, Venugopal K R4, Iyengar S S5, Patnaik L M6
1Ramya R S, Research Scholar in the Department of Computer Science Engineering, University Visweswaraya College of Engineering, Bangalore, India.
2Darshan M, Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bangalore, India.
3Dr. Sejal Santosh Nimbhorkar, Associate Professor at B N M Institute of Technology, Bangalore, India.
4Venugopal K R , Principal, University Visvesvaraya College of Engineering, Bangalore University, Bangalore, India.
5S S Iyengar, Professor, Florida International University, USA.
6L M Patnaik, Professor, Indian Institute of Science, Bangalore, India.
Manuscript received on November 26, 2019. | Revised Manuscript received on December 30, 2019. | Manuscript published on December 30, 2019. | PP: 5445-5457 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B2993129219/2019©BEIESP | DOI: 10.35940/ijeat.B2993.129219
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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: In today’s world, due to the steep rise in internet users, Community Question Answering (CQA) has attracted many research communities. In order to provide the correct and perfect answer to the user asked question from a given large collection of text data, understanding the question properly to suggest a precise answer is a challenging task. Therefore, Question Answering (QA) system is a challenging task than a common information retrieval task done by many search engines. In this paper, an automatic prediction of the quality of CQA answers is proposed. This is accomplished by using five well known machine learning algorithms. Usually, questions asked by the user are based on a topic or theme. We try to exploit this feature in our work by identifying the category of the question posted and further map with the corresponding question. Similarly, for the answers posted by the multiple user’s are processed as answer for category mapping. Here, the results show that for Question Classification (QA), Linear Support Vector Classification (LSVC) is found to be the best classifier and Multinomial Logistic Regression (MLR) is the most suitable for Answer Classification (AC). The MS Macro dataset is used as the underlying dataset for retrieving and testing the question and answer classifiers. The Yahoo Answers are used as a golden reference during the testing throughout our experiments. Experiments results show that the proposed technique is efficient and outperforms Metzler and Kanungo’s (MK++)  while providing the best answer summary satisfying the user’s queries.
Keywords: Question answering, Answer biased summaries, Information Retrieval, Classification, Document summarization.