Automatic text summarization using fuzzy inference

Mehdi Jafari, Jing Wang, Yongrui Qin, Mehdi Gheisari, Amir Shahab Shahabi, Xiaohui Tao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

Due to the high volume of information and electronic documents on the Web, it is almost impossible for a human to study, research and analyze this volume of text. Summarizing the main idea and the major concept of the context enables the humans to read the summary of a large volume of text quickly and decide whether to further dig into details. Most of the existing summarization approaches have applied probability and statistics based techniques. But these approaches cannot achieve high accuracy. We observe that attention to the concept and the meaning of the context could greatly improve summarization accuracy, and due to the uncertainty that exists in the summarization methods, we simulate human like methods by integrating fuzzy logic with traditional statistical approaches in this study. The results of this study indicate that our approach can deal with uncertainty and achieve better results when compared with existing methods.

LanguageEnglish
Title of host publication2016 22nd International Conference on Automation and Computing (ICAC)
EditorsProf. Zhijie Xu, Dr. Jing Wang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages256-260
Number of pages5
ISBN (Electronic)9781862181311
DOIs
Publication statusPublished - 24 Oct 2016
Event22nd International Conference on Automation and Computing - Colchester, United Kingdom
Duration: 7 Sep 20168 Sep 2016
Conference number: 22

Conference

Conference22nd International Conference on Automation and Computing
Abbreviated titleICAC 2016
CountryUnited Kingdom
CityColchester
Period7/09/168/09/16

Fingerprint

Fuzzy Inference
Summarization
Fuzzy inference
Fuzzy logic
Applied Probability
Uncertainty
Statistics
Fuzzy Logic
High Accuracy
Electronics
Text
Human
Concepts
Context

Cite this

Jafari, M., Wang, J., Qin, Y., Gheisari, M., Shahabi, A. S., & Tao, X. (2016). Automatic text summarization using fuzzy inference. In P. Z. Xu, & D. J. Wang (Eds.), 2016 22nd International Conference on Automation and Computing (ICAC) (pp. 256-260). [7604928] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IConAC.2016.7604928
Jafari, Mehdi ; Wang, Jing ; Qin, Yongrui ; Gheisari, Mehdi ; Shahabi, Amir Shahab ; Tao, Xiaohui. / Automatic text summarization using fuzzy inference. 2016 22nd International Conference on Automation and Computing (ICAC). editor / Prof. Zhijie Xu ; Dr. Jing Wang. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 256-260
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Jafari, M, Wang, J, Qin, Y, Gheisari, M, Shahabi, AS & Tao, X 2016, Automatic text summarization using fuzzy inference. in PZ Xu & DJ Wang (eds), 2016 22nd International Conference on Automation and Computing (ICAC)., 7604928, Institute of Electrical and Electronics Engineers Inc., pp. 256-260, 22nd International Conference on Automation and Computing, Colchester, United Kingdom, 7/09/16. https://doi.org/10.1109/IConAC.2016.7604928

Automatic text summarization using fuzzy inference. / Jafari, Mehdi; Wang, Jing; Qin, Yongrui; Gheisari, Mehdi; Shahabi, Amir Shahab; Tao, Xiaohui.

2016 22nd International Conference on Automation and Computing (ICAC). ed. / Prof. Zhijie Xu; Dr. Jing Wang. Institute of Electrical and Electronics Engineers Inc., 2016. p. 256-260 7604928.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T1 - Automatic text summarization using fuzzy inference

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AB - Due to the high volume of information and electronic documents on the Web, it is almost impossible for a human to study, research and analyze this volume of text. Summarizing the main idea and the major concept of the context enables the humans to read the summary of a large volume of text quickly and decide whether to further dig into details. Most of the existing summarization approaches have applied probability and statistics based techniques. But these approaches cannot achieve high accuracy. We observe that attention to the concept and the meaning of the context could greatly improve summarization accuracy, and due to the uncertainty that exists in the summarization methods, we simulate human like methods by integrating fuzzy logic with traditional statistical approaches in this study. The results of this study indicate that our approach can deal with uncertainty and achieve better results when compared with existing methods.

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Jafari M, Wang J, Qin Y, Gheisari M, Shahabi AS, Tao X. Automatic text summarization using fuzzy inference. In Xu PZ, Wang DJ, editors, 2016 22nd International Conference on Automation and Computing (ICAC). Institute of Electrical and Electronics Engineers Inc. 2016. p. 256-260. 7604928 https://doi.org/10.1109/IConAC.2016.7604928