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Fuzzy Logic in Text Summarization: Theory, Algorithm, Evaluation, and Applications

Call for Papers

From sentence extraction to abstract generation, Text Summarization has been regarded as the most difficult but extremely promising field in text mining. Furthermore, it has a wide range of applications in various areas such as data science, social media analysis, and big data.

In recent years, various algorithms have been developed ranging from machine learning based, statistics based, and rule based to state transition systems. In each category, the distance and similarity estimation has always been a key issue, and the accuracy has always been the major challenge. Fuzzy Logic may play an important role in reflecting the fuzziness and imprecision of the real world in this area.

The aim of the special issue is to offer an opportunity for researchers to explore the applications of Fuzzy Logic in all aspects of Text Summarization: abstractive or extractive; single or multiple documents; generic or query-based. In particular, we welcome manuscripts from methodology evaluation which has been regarded as a challenging task.

Potential topics include but are not limited to the following:

  • Machine learning based approaches
  • Statistics based approaches
  • Clustering in Text Summarization
  • Graph based approaches
  • Logic rule systems
  • Natural language processing related research
  • Methodology evaluation
  • Fuzzy distance and similarity estimation

Authors can submit their manuscripts through the Manuscript Tracking System at

Submission DeadlineFriday, 2 March 2018
Publication DateJuly 2018

Papers are published upon acceptance, regardless of the Special Issue publication date.

Lead Guest Editor

  • Fei Liu, La Trobe University, Melbourne, Australia

Guest Editors

  • Hung V. Le, Hanoi University of Mining and Geology, Hanoi, Vietnam
  • Mehdi Assefi, University of Georgia, Athens, USA