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 contributionpeer-review

37 Citations (Scopus)


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.

Original 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.
Number of pages5
ISBN (Electronic)9781862181311
Publication statusPublished - 24 Oct 2016
Event22nd International Conference on Automation and Computing - Colchester, United Kingdom
Duration: 7 Sep 20168 Sep 2016
Conference number: 22


Conference22nd International Conference on Automation and Computing
Abbreviated titleICAC 2016
Country/TerritoryUnited Kingdom


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