Text Summarization using QA Corpus for User Interaction Model QA System

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Author(s)

K.Karpagam 1 A. Saradha 2 K.Manikandan 3 K.Madusudanan 1

1. Dr. Mahalingam College of Engineering & Technology, Pollachi

2. Institute of Road Transport and Technology, Erode

3. Vellore Institute of Technology, Vellore

* Corresponding author.

DOI: https://doi.org/10.5815/ijeme.2020.03.04

Received: 9 Mar. 2020 / Revised: 18 Mar. 2020 / Accepted: 25 Mar. 2020 / Published: 8 Jun. 2020

Index Terms

Question Answering System, QA corpus, Text summarization, Machine Learning

Abstract

Document summarization is capable of generating user query relevant, precise summaries from the original document for user needs. To reduce the response time summary generation, QA corpus is built for similar questions and answer with help of learning model. It has been trained and tested by Quora duplicate and Yahoo! Answer datasets. The large QA corpus has been dynamically clustered with semantic features paves a way for efficient document’s retrieval. Answers are produced from datasets or generate summaries for unanswerable from the available sources. Results obtained from statistical significance test with hypothesis testing and evaluation with standard metrics proves the significant improvement in generating text summarization using QA corpus. The outcome is better in the producing close proximity of answers for the given user query.

Cite This Paper

K.Karpagam, A. Saradha, K.Manikandan, K.Madusudanan. "Text Summarization using QA Corpus for User Interaction Model QA System", International Journal of Education and Management Engineering(IJEME), Vol.10, No.3, pp.33-41, 2020. DOI: 10.5815/ijeme.2020.03.04

Reference

[1].Rasmita Rautray, Rakesh Chandra Balabantaray&Anisha Bhardwaj ., Document  Summarization  using Sentence Features, International  Journal of Information Retrieval  Res(IJIRR), Volume 5 Issue 1, pp.36-47, 2015.

[2].Ming Tan, Cicero dos Santos, Bing Xiang & Bowen Zhou, Improved Representation Learning for Question Answer Matching’, Proceedings of  the 54th Annual Meeting of the Association for  Computational Linguistics, August, pp. 7-12,2016. 

[3].Denis Savenkov & Eugene Agichtein, ‘CRQA: Crowd-Powered Real-Time Automatic Question  Answering System’, Proceedings, The Fourth AAAI Conference on  Human  Computation & Crowdsourcing , Association for the Advancement of  Artificial  Intelligence, pp.189 -198, 2016.

[4].Dorota Gowacka, Tuukka Ruotsalo , Ksenia Konyushkova , Kumaripaba Athukorala , Samuel Kaski& Giulio Jacucci,  Directing Exploratory Search: Reinforcement Learning  from  User Interactions with Keywords, IUI’13, Copyright ACM, pp.117-127, 2013.

[5].Jan Frederik Forst, Anastasios Tombros& Thomas Roelleke, ‘Less Is More: Maximal Marginal Relevance as a Summarization Feature’, Advances in Information Retrieval Theory, ICTIR, pp. 350-353, 2009.

[6].Farshad Kiyoumarsi, , Evaluation of Automatic Text Summarizations Based on Human Summaries, 2nd  GLOBAL CONFERENCE on LINGUISTICS & FOREIGN LANGUAGE TEACHING, LINELT-2014, Procedia - Social & Behavioral Sciences,  ELSEVIER,  pp. 83 – 91, 2014.

[7].Gunnar Schröder, Maik Thiele &Wolfgang Lehner , , ‘Setting Goals & Choosing Metrics for    Recommender System Evaluations ‘, 5th ACM Conference on Dresden University of Technology   Recommender Systems Chicago, October 23th, 2011.

[8].Youzheng Wu, Chiori Hori, Hideki Kashioka & Hisashi Kawai, , ‘Leveraging social  QA collections for improving complex question answering’, Computer Speech & Language, Volume 29 Issue 1, pp.1-19,2015.

[9].Hiroyuki Sakai & Shigeru Masuyama, ‘A Multiple-Document Summarization System with User    Interaction’, In Proceedings of the 20th International Conference on Computational Linguistics, COLING ‘04, C04-1144, ACL Anthology, pp.1001- 007,2004.

[10].Gang Liu & Tianyong Hao, ‘User-based Question Recommendation for Question Answering System’, International Journal of Information & Education Technology volume 2, Issue 3, pp. 243-246, 2012.

[11].Karpagam.K & Saradha.A, “Text Summarization using Machine Learning Approaches for Question  Answering System”, International Journal of Advances in Computer and Electronics Engineering, ISSN: 2456 - 3935, Volume 4, Issue 2, pp.1–5, , 2019.

[12].John, A., Premjith, P.S., Wilscy, M ,” Extractive multi-document summarization using  population- based multi-criteria optimization”, Expert System Application 86, pp.385–39, 2017.

[13].Sanchez-Gomez, J.M., Vega-Rodríguez, M.A., Pérez, C.J.,”Extractive multi-document text summarization using a multi-objective artificial bee colony optimization approaches”, Knowledge-Based  System, 2017.

[14].Al-Radaideh, Q.A., Bataineh, D.Q, ” A hybrid approach for arabic text summarization using domain knowledge and genetic algorithms”, Cognitive Computer, 2018.

[15].Rautray, R., Balabantaray, R.C. “An evolutionary framework for multi-document summarization using cuckoo search approach”, Mdscsa. Application Computer Inform. 14 (2),  pp.134–144, 2018.

[16].Litvak, M., Vanetik, N., Last, M., Churkin, E.. Museec,, A multilingual text  summarization tool, pp. 73–78, 2016.

[17].Thomas, S., Beutenmüller, C., de la Puente, X., Remus, R., Bordag, S, “Extractive text Summarizer”, Proceedings of the SIGDIAL 2015 Conference, pp. 260–269, 2015.

[18].Abdelkrime, A., Djamel Eddine, Z., Khaled Walid, H. ,“All summarizer system at multilingual single and multi-document summarization” Proceedings of the SIGDIAL 2015 Conference,pp.237–244, 2015.

[19].Litvak, M., Vanetik, N., Last, M., Churkin, E.. Museec,” A multilingual text summarization tool”, pp. 73–78,2016,.

[20].Thomas, S., Beutenmüller, C., de la Puente, X., Remus, R., Bordag, S,,., “Extractive text Summarizer”, Proceedings of the SIGDIAL 2015 Conference, pp. 260–269,2015.

[21].Benjamin Timmermans, Lora Aroya& Chris Welty ,“Crowd sourcing ground truth for Question Answering using Crowd Truth”, Web Science, Oxford, United Kingdom, ACM,2015.

[22].Hiroyuki Sakai & Shigeru Masuyama, ,”A Multiple-Document Summarization System with User  Interaction”, In Proceedings of the 20th International Conference on Computational   Linguistics, COLING ‘04, C04-1144, ACL Anthology, pp.1001- 1007,2004.

[23].Gunnar Schröder, Maik Thiele &Wolfgang Lehner, ‘Setting Goals & Choosing Metrics for     Recommender System Evaluations ‘, 5th ACM   Conference on Dresden University of Technology   Recommender Systems Chicago, October 23th, 2011.