Text Summarization Based on Classification Using ANFIS

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


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.


Text summarization Neural network Fuzzy logic ANFIS 



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.


  1. 1.
    Kumar, Y.J., Goh, O.S., Halizah, B., Ngo, H.C., Puspalata, C.: A review on automatic text summarization approaches. J. Comput. Sci. 12(4), 178–190 (2016)CrossRefGoogle Scholar
  2. 2.
    Khan, A., Salim, N., Kumar, Y.J.: Genetic semantic graph approach for multi-document abstractive summarization. In: Fifth International Conference on Digital Information Processing and Communications (ICDIPC), pp. 173–181 (2015)Google Scholar
  3. 3.
    Keyan, M., Srinivasagan, K.: Multi-document and multi-lingual summarization using neural networks. In: International Conference on Recent Trends in Computational Methods, Communication and Controls (ICON3C), pp. 11–14 (2012)Google Scholar
  4. 4.
    Patil, M.P.D., Kulkarni, N.J.: Text summarization using fuzzy logic. Int. J. Innov. Res. Adv. Eng. (IJIRAE) 1(3), 42–45 (2014)Google Scholar
  5. 5.
    Rucha, S., Apte, S.: Improvement of text summarization using fuzzy logic based method. OSR J. Comput. Eng. (IOSRJCE). 5(6), 5–10 (2012)Google Scholar
  6. 6.
    Megala, S.S., Kavitha, A., Marimuthu, A.: Enriching text summarization using fuzzy logic. Int. J. Comput. Sci. Inf. Technol. 5, 863–867 (2014)Google Scholar
  7. 7.
    Sarda, A., Kulkarni, A.: Text summarization using neural network and rhetorical structure theory. Int. J. Adv. Res. Comput. Commun. Engineering, IJARCCE 4(6), 49–52 (2015)Google Scholar
  8. 8.
    Suanmali, L., Salim, N., Binwahlan, M.S.: Fuzzy logic based method for improving text summarization. Int. J. Comput. Sci. Inf. Secur. 2(1) (2009)Google Scholar
  9. 9.
    Kumar, Y.J., Salim, N., Abuobieda, A., Albaham, A.T.: Multi document summarization based on news components using fuzzy cross-document relations. Appl. Soft Comput. 21, 265–279 (2014)CrossRefGoogle Scholar
  10. 10.
    Babar, S.A., Patil, P.D.: Improving performance of text summarization. Proc. Comput. Sci. 46, 354–363 (2015)CrossRefGoogle Scholar
  11. 11.
    Albertos, P., Sala, A.: Fuzzy logic controllers. Advantages and drawbacks. In: VIII International Congress of Automatic Control, vol. 3, pp. 833–844 (1998)Google Scholar
  12. 12.
    Fattah, M.A., Ren, F.: Automatic text summarization. World Acad. Sci. Eng. Technol. 13, 192–195 (2008)Google Scholar
  13. 13.
    Loganathan, C., Girija, V.: Investigations on hybrid learning in ANFIS. Int. J. Eng. Res. Appl. 4(10), 31–37 (2014)Google Scholar
  14. 14.
    Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings of International Conference Research on Computational Linguistics, pp. 1–15 (1997)Google Scholar
  15. 15.
    Lim, E.A., Jayakumar, Y.: A study of neuro-fuzzy system in approximation-based problems. Matematika 24, 113–130 (2008)CrossRefGoogle Scholar
  16. 16.
    Moh’d Arikat, Y.: Subtractive neuro-fuzzy modeling techniques applied to short essay auto-grading problem. In: International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), pp. 889–895 (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yogan Jaya Kumar
    • 1
    Email author
  • 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

Personalised recommendations