Text Summarization Technique by Sentiment Analysis and Cuckoo Search Algorithm

  • Shrabanti Mandal
  • Girish Kumar SinghEmail author
  • Anita Pal
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1025)


To manage the huge information, summarization is one of the most essential tasks. There are many techniques available for this purpose, yet this is a challenge to produce the optimum solution. This paper proposes an approach for text summarization based on sentiment analysis and cuckoo search algorithm. For solving the optimization problem in several areas, the cuckoo search algorithm is used. The cuckoo search basically is a type of nature-inspired algorithms. It is efficient for solving the global optimization problem as it is capable to proceed by maintaining balance between local and global random walks. Here we use cuckoo search algorithm with sentiment score for summarizing the text document. The experimental analysis uses benchmark database. The outcome of the proposed model has been compared in terms of ROUGE score with some existing and some human-generated output.


Text summarization Cuckoo search Lévy flight Gauss distribution 


  1. 1.
    Mareli, M., Twala, B.: Applied Computing and Informatics (2017).
  2. 2.
    Zheng, H., Zhou, Y.: A novel cuckoo search algorithm based on Gauss distribution. J. Comput. Inf. Syst. 8(10), 4193–4200 (2012)Google Scholar
  3. 3.
    Zaw, M.M., Mon, E.E.: Web document clustering using Gauss distribution based cuckoo search clustering algorithm. Int. J. Sci. Eng. Technol. Res. 3(13), 2945–2949 (2014)Google Scholar
  4. 4.
    Ho, S.D., Vo, V.S., Le, T.M., Nguyen, T.T.: Economic emission load dispatch with multiple fuel optings using cuckoo search algorithm with Gaussian and Cauchy distributions. Int. J. Energy Inf. Commun. 5 (5), 39–54 (2014)Google Scholar
  5. 5.
    Nguyen, T.T., Vo, D.N., Dinh, B.H.: Cuckoo search algorithm using different distributions for short term hydrothermal scheduling with reservoir volume constraint. Int. J. Electr. Eng. Inf. 8(1), 76–92 (2016)Google Scholar
  6. 6.
    Roy, S., Mallick, A., Chowdhury, S.S., Roy, S.: A novel approach on cuckoo search algorithm using Gamma distribution. In: Second International Conference on Electronics and Communication Systems (2015)Google Scholar
  7. 7.
    Tusiy, S.I., Shawkat, N., Ahmed, M.A., Panday, B., Sakib, N.: Comparative analysis on improved Cuckoo search algorithm and artificial Bee colony algorithm on continuous optimization problems. Int. J. Adv. Res. Artif. Intell. 4(2), 14–19 (2015)Google Scholar
  8. 8.
    Tuba, M., Subotic, M., Stanarevic, N.: Modified Cuckoo search algorithm for unconstrained optimization problems. In: Proceedings of the European Computing Conference (2011)Google Scholar
  9. 9.
    Zheng, H., Zhou, Y.: A novel cuckoo search optimization algorithm base on Gauss distribution. J. Comput. Inf. Syst. 8(10), 4193–4200 (2012)Google Scholar
  10. 10.
    Richmond, W.K.: Teachers and machines: an introduction to the theory and practice of programmed learning: Collins (1965)Google Scholar
  11. 11.
    Shaikh, M.A., Prendinger, H., Mitsuru, I.: Assessing sentiment of text by semantic dependency and contextual valence analysis. In: Presented at the Proceedings of the 2nd International Conference on Affective Computing and Intelligent Interaction, Lisbon, Portugal (2007)Google Scholar
  12. 12.
    Mandal, S., Singh, G.K., Pal, A.: PSO based text summarization approach using sentiment analysis. In: Advances in Intelligent Systems and Computing, vol. 810, pp. 845–854 (2019).
  13. 13.
    Nenkova, A.: Automatic text summarization of newswire: lessons learned from the document understanding conference. In: AAAI (2005)Google Scholar
  14. 14.
    Rautray, R., Balabantaray, R.C.: An evolutionary framework for Multi Document Summarization using Cuckoo Search Approach: MDSCSA. Accepted in Appl. Comput. Inf. (2017).
  15. 15.
    Sarkar, K.: Automatic single document text summarization using key concepts in documents. J. Inf. Process. Syst. 9(4), 602–620 (2013)CrossRefGoogle Scholar
  16. 16.
    Lin, C.Y., Hovy, E.: Automatic evaluation of summaries using n-gram co-occurrence statistics. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, vol. 1, pp. 71–78. Association for Computational Linguistics (2003)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Shrabanti Mandal
    • 1
  • Girish Kumar Singh
    • 1
    Email author
  • Anita Pal
    • 2
  1. 1.Department of Computer Science & ApplicationsDr. Harisingh Gour Central UniversitySagarIndia
  2. 2.Department of MathematicsNational Institute of Technology, DurgapurDurgapurIndia

Personalised recommendations