A Hybrid Text Summarization Approach

Authors

  • Shrabanti Mandal Department of Computer Science and Applications, Dr. Harisingh Gour Central University, Sagar, Madya Pradesh
  • Girish Kumar Singh Department of Computer Science and Applications, Dr. Harisingh Gour Central University, Sagar, Madya Pradesh
  • Anita Pal Department of Mathematics, National Institute of Technology, Durgapur, West Bengal

DOI:

https://doi.org/10.26713/jims.v9i3.760

Keywords:

Text summarization, Sentiment analysis, Feature extraction, Fuzzy concept

Abstract

Today, internet is the storage of huge information. Therefore it is very serious issue to get data fast and efficiently. Text summarization plays an important role in the field of information retrieval. Text summarization is a process of representing a text in concise way with same sense. This hybrid approach mainly based on extractive summarization. The proposed approach combines the concept of statistical measure, sentiment analysis and finally uses the concept of fuzzy logic to select sentence. Based on the level of importance of the sentence, summary is created.

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References

R.M. Alguliev, R.M. Aliguliyev and C.A. Mehdiyev, Sentence selection for generic document summarization using an adaptive differential evolution algorithm, Swarm and Evolutionary Computation 1 (4) (2011), 213 – 222.

R.M. Aliguliyev, Automatic document summarization by sentence extraction, Journal of Computational Technologies 12 (5) (2007), 5 – 15.

S.A. Babar and P.D. Patil, Improving performance of text summarization, International Conference on Information and Communication Technologies (ICICT 2014), Procedia Computer Science 46 (2015), 354 – 363.

J. Carbonell and J. Goldstein, The use of MMR, diversity-based reranking for reordering documents and producing summaries, in Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 335 – 336, ACM (1998).

D. Das and S. Bandyopadhyay, Word to sentence level emotion tagging for bengali blogs, ACLIJCNLP 2009, pp. 149 – 152, Suntec, Singapore (2009).

D. Das and S. Bandyopadhyay, Developing bengali WordNet affect for analyzing emotion, ICCPOL-2010, California, USA (2010).

A. Hogenboom, F. Boon and F. Frasincar, A Statistical Approach to Star Rating Classification of Sentiment, in Management Intelligent Systems, Vol. 171, J. Casillas, F.J. Martí­nez-López and J.M. Corchado Rodrí­guez (eds.), Springer, Berlin”Heidelberg (2012), pp. 251 – 260.

J. Karlgren, M. Sahlgren, F. Olsson, F. Espinoza and O. Hamfors, Usefulness of Sentiment Analysis, in Advances in Information Retrieval, Vol. 7224, R. Baeza-Yates, A. Vries, H. Zaragoza, B.B. Cambazoglu, V. Murdock, R. Lempel and F. Silvestri (eds.), Springer, Berlin”Heidelberg (2012), pp. 426 – 435.

Y. Ko and J. Seo, An effective sentence-extraction technique using contextual information and statistical approaches for text summarization, Pattern Recognition Letters 29 (9) (2008), 1366 – 1371.

C.J. Leuba, Man: A General Psychology, Holt, Rinehart and Winston (1961).

H.P. Luhn, The automatic creation of literature abstracts, IBM Journal of Research and Development 2 (2) (1958), 159 – 165.

I. Mani and M.T. Maybury (eds.), Advances in Automatic Text Summarization, Vol. 293, IT Press, Cambridge (1999).

Y. Ouyang, W. Li, S. Li and Q. Lu, Applying regression models to query-focused multi-document summarization, Information Processing and Management 47 (2) (2011), 227 – 237.

B. Pang and L. Lee, Opinion Mining and Sentiment Analysis, Found. Trends Inf. Retr. 2 (2008), 1 – 135.

D.R. Radev, S. Blair-Goldensohn and Z. Zhang, Experiments in Single and Multi-Document Summarization using MEAD, Ann Arbor, 1001, 48109 (2001).

D.R. Radev, E. Hovy and K. McKeown, Introduction to the special issue on summarization, Computational Linguistics 28 (4) (2002), 399 – 408.

D.R. Radev, H. Jing, M. Stys and D. Tam, Centroid-based summarization of multiple documents, Information Processing and Management 40 (6) (2004), 919 – 938.

W.K. Richmond, Teachers and Machines: An Introduction to the Theory and Practice of Programmed Learning, Collins (1965).

S. Gholamrezazadeh and M.A. Salehi, A Comprehensive Survey on Text Summarization Systems, IEEE (2009).

K. Sarkar, Syntactic trimming of extracted sentences for improving extractive multi document summarization, Journal of Computing 2 (7) (2010), 177 – 184.

M.A. Shaikh, H. Prendinger and I. Mitsuru, Assessing sentiment of text by semantic dependency and contextual valence analysis, presented at the Proceedings of the 2nd International Conference on Affective Computing and Intelligent Interaction, Lisbon, Portugal (2007).

V. Gupta and G.S. Lehal, A survey of text summarization techniques, Journal of Emerging Technologies in Web Intelligence 2 (3) (2010), 258 – 268.

X. Wan, Wan Using only cross-document relationships for both generic and topic-focused multidocument summarizations, Information Retrieval 11 (2008), 25 – 29.

C.S. Yadav and A. Sharan, Hybrid approach for single text document summarization using statistical and sentiment features, International Journal of Information Retrieval Research 5 (4) (2015), 46 – 70.

J.Y. Yeh, H.R. Ke, W.P. Yang and I.H. Meng, Text summarization using a trainable summarizer and latent semantic analysis, Information Processing and Management 41 (1) (2005), 75 – 95.

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CITATION

How to Cite

Mandal, S., Singh, G. K., & Pal, A. (2017). A Hybrid Text Summarization Approach. Journal of Informatics and Mathematical Sciences, 9(3), 547–555. https://doi.org/10.26713/jims.v9i3.760

Issue

Vol. 9 No. 3 (2017)

Section

Research Articles

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