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Text Summarization Based on Classification Using ANFIS

  • Yogan Jaya Kumar
  • Fong Jia Kang
  • Ong Sing Goh
  • Atif Khan
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 710)

Abstract

The information overload faced by today’s society has created a big challenge for people who want to look for relevant information from the internet. There are a lot of online documents available and digesting such large texts collection is not an easy task. Hence, automatic text summarization is required to automate the process of summarizing text by extracting only the salient information from the documents. In this paper, we propose a text summarization model based on classification using Adaptive Neuro-Fuzzy Inference System (ANFIS). The model can learn to filter high quality summary sentences. We then compare the performance of our proposed model with the existing approaches which are based on neural network and fuzzy logic techniques. ANFIS was able to alleviate the limitations in the existing approaches and the experimental finding of this study shows that the proposed model yields better results in terms of precision, recall and F-measure on the Document Understanding Conference (DUC) data corpus.

Keywords

Text summarization Neural network Fuzzy logic ANFIS 

Notes

Acknowledgements

This research work supported by Universiti Teknikal Malaysia Melaka and Ministry of Education, Malaysia under the Research Acculturation Grant Scheme (RAGS) No. RAGS/1/2015/ICT02/FTMK/02/B00124.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yogan Jaya Kumar
    • 1
  • Fong Jia Kang
    • 1
  • Ong Sing Goh
    • 1
  • Atif Khan
    • 2
  1. 1.Faculty of Information and Communication TechnologyUniversiti Teknikal Malaysia MelakaDurian TunggalMalaysia
  2. 2.Department of Computer ScienceIslamia College PeshawarPeshawarPakistan

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