CSTS: Cuckoo Search Based Model for Text Summarization

Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 517)

Abstract

Exponential growth of information in the web became infeasible for user to sieve useful information very quickly. So solution to such problem now a day is text summarization. Text summarization is the process of creating condensed version of original text by preserving important information in it. This paper presents for the first time a nature inspired cuckoo search optimization algorithm for optimal selection of sentences as summary sentence of intelligent text summarizer. The key aspects of proposed summarizer focus on content coverage and length while reducing redundant information in the summaries. To solve this optimization problem, this model uses inter-sentence relationship and sentence-to-document relationship by considering widely used similarity measure cosine similarity. The inputs for this model are taken from DUC dataset. Whereas the result is evaluated by ROUGE tool and compared with state-of-the-art approaches, in which our model in multi-document summarization have shown significant result than others.

Keywords

Summarization Content coverage Length Redundancy Cuckoo search 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Ahmed BS, Abdulsamad TS, Potrus MY(2015) Achievement of minimized combinatorial test suite for configuration-aware software functional testing using the Cuckoo search algorithm. Information and Software Technology.66:13–29.Google Scholar
  2. 2.
    Alguliev RM, Aliguliyev RM, Hajirahimova MS(2012) GenDocSum + MCLR: Generic document summarization based on maximum coverage and less redundancy. Expert Systems with Applications. 39(16):12460–73.Google Scholar
  3. 3.
    Alguliev RM, Aliguliyev RM, Hajirahimova MS, Mehdiyev CA(2011a) MCMR: Maximum coverage and minimum redundant text summarization model. Expert Systems with Applications. 38(12):14514–22.Google Scholar
  4. 4.
    Alguliev RM, Aliguliyev RM, Isazade NR. CDDS(2013) Constraint-driven document summarization models. Expert Systems with Applications. 40(2):458–65.Google Scholar
  5. 5.
    Alguliev RM, Aliguliyev RM, Mehdiyev CA(2011b) Sentence selection for generic document summarization using an adaptive differential evolution algorithm. Swarm and Evolutionary Computation. 1(4):213–22.Google Scholar
  6. 6.
    Araghi S, Khosravi A, Creighton D(2015) Intelligent cuckoo search optimized traffic signal controllers for multi-intersection network. Expert Systems with Applications. 42(9):4422–31.Google Scholar
  7. 7.
    Bhandari AK, Singh VK, Kumar A, Singh GK(2014) Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Systems with Applications. 41(7):3538–60.Google Scholar
  8. 8.
    Carbonell J, Goldstein J(1998) The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval (335–336). ACM.Google Scholar
  9. 9.
    Celikyilmaz A, Hakkani-Tur D(2010) A hybrid hierarchical model for multi-document summarization. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (pp. 815–824). Association for Computational Linguistics.Google Scholar
  10. 10.
    Cobos C, Muñoz-Collazos H, Urbano-Muñoz R, Mendoza M, León E, Herrera-Viedma E(2014) Clustering of web search results based on the cuckoo search algorithm and balanced Bayesian information criterion. Information Sciences. 281:248–64.Google Scholar
  11. 11.
    Dash P, Saikia LC, Sinha N(2014) Comparison of performances of several Cuckoo search algorithm based 2DOF controllers in AGC of multi-area thermal system. International Journal of Electrical Power & Energy Systems. 55:429–36.Google Scholar
  12. 12.
    Dos Santos Coelho L, Klein CE, Sabat SL, Mariani VC(2014) Optimal chiller loading for energy conservation using a new differential cuckoo search approach. Energy. 75:237–43.Google Scholar
  13. 13.
    He R, Qin B, Liu T(2012) A novel approach to update summarization using evolutionary manifold-ranking and spectral clustering. Expert Systems with Applications. 39(3):2375–84.Google Scholar
  14. 14.
    Huang L, He Y, Wei F, Li W(2010) Modeling document summarization as multi-objective optimization. InIntelligent Information Technology and Security Informatics (IITSI), 382–386.Google Scholar
  15. 15.
    Kanagaraj G, Ponnambalam SG, Jawahar N(2014) Reliability-based total cost of ownership approach for supplier selection using cuckoo-inspired hybrid algorithm. The International Journal of Advanced Manufacturing Technology. 1–6.Google Scholar
  16. 16.
    Lim WC, Kanagaraj G, Ponnambalam SG(2012) Cuckoo search algorithm for optimization of sequence in pcb holes drilling process. In Emerging trends in science, engineering and technology (207–216). Springer India.Google Scholar
  17. 17.
    Lin CY, Hovy E(2003) 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-Volume 1 (71–78). Association for Computational Linguistics.Google Scholar
  18. 18.
    Lloret E, Palomar M(2012) Text summarisation in progress: a literature review. Artificial Intelligence Review. 37(1):1–41.Google Scholar
  19. 19.
    Mohamad AB, Zain AM, Nazira Bazin NE(2014) Cuckoo search algorithm for optimization problems—a literature review and its applications. Applied Artificial Intelligence. 28(5):419–48.Google Scholar
  20. 20.
    Mulani K, Talukdar P, Das A, Alagirusamy R(2015) Performance analysis and feasibility study of ant colony optimization, particle swarm optimization and cuckoo search algorithms for inverse heat transfer problems. International Journal of Heat and Mass Transfer. 89:359–78.Google Scholar
  21. 21.
    Nguyen TT, Truong AV(2015) Distribution network reconfiguration for power loss minimization and voltage profile improvement using cuckoo search algorithm. International Journal of Electrical Power & Energy Systems. 68:233–42.Google Scholar
  22. 22.
    Nguyen TT, Vo DN(2015) Modified cuckoo search algorithm for short-term hydrothermal scheduling. International Journal of Electrical Power & Energy Systems. 65:271–81.Google Scholar
  23. 23.
    Radev DR, Hovy E, McKeown K(2002) Introduction to the special issue on summarization. Computational linguistics. 28(4):399–408.Google Scholar
  24. 24.
    Radev DR, Jing H, Styś M, Tam D(2004) Centroid-based summarization of multiple documents. Information Processing & Management. 40(6):919–38.Google Scholar
  25. 25.
    Rautray R, Balabantaray RC(2015) Comparative Study of DE and PSO over Document Summarization. In Intelligent Computing, Communication and Devices,371–377), Springer India.Google Scholar
  26. 26.
    Wang J, Jiang H, Wu Y, Dong Y(2015) Forecasting solar radiation using an optimized hybrid model by Cuckoo Search algorithm. Energy. 81627–44.Google Scholar
  27. 27.
    Yang XS, Deb S(2009) Cuckoo search via Lévy flights. In Nature & Biologically Inspired Computing, 2009. NaBIC 2009. (210–214). IEEE.Google Scholar
  28. 28.
    Yang XS, Deb S(2010) Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation. 1(4):330–43.Google Scholar
  29. 29.
    Zineddine M(2015) Vulnerabilities and mitigation techniques toning in the cloud: A cost and vulnerabilities coverage optimization approach using Cuckoo search algorithm with Lévy flights. Computers & Security. 48:1–8.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSiksha ‘O’ Anusandhan UniversityBhubaneswarIndia
  2. 2.Department of Computer ScienceIIITBhubaneswarIndia

Personalised recommendations