[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Enhanced continuous and discrete multi objective particle swarm optimization for text summarization

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Reviews from various domains is being posted in web increasingly day by day. Analyzing this enormous content would be useful in decision making for various stakeholders. Text summarization techniques generate concise summaries including sentiments which are useful in analyzing the large content. So text summarization systems become significant in analyzing this huge content. The summaries are generated based on important features using multi objective approaches where sufficient literature is not available. Major limitations of text summarization systems are scalability and performance. Two variants of multi objective optimization techniques such as Discrete and Continuous which work under the principles of particle swarm optimization (PSO) for extractive summarization of reviews had been proposed for performance improvement. The performance is validated using Recall-Oriented Understanding for Gisting Evaluation (ROUGE), Success Counting (SC) and Inverted Generational Distance (IGD). Based on the experimental results it is found that the system is effective using multi-objective PSO algorithm when compared to other state-of-art approaches like Liu’s approach feature based binary particle swarm optimization and etc. for feature based review summarization.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the 2004 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining—KDD’04, pp. 168–177 (2004)

  2. Carenini, G., Cheung, J.C.K., Pauls, A.: Multi-document summarization of evaluative text. Comput. Intell. 29(4), 545–576 (2012)

    Article  MathSciNet  Google Scholar 

  3. Gamon, M., Aue, A., Corston-Oliver, S., Ringger, E.: Pulse: mining customer opinions from free text. In: International Symposium on Intelligent Data Analysis VI, pp. 121–132, (2005)

  4. Zhuang, L., Jing, F., Zhu, X.-Y.: Movie review mining and summarization. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management—CIKM’06, pp. 43–50 (2006)

  5. Liu, M., Fang, Y., Choulos, A.G., et al.: Product review summarization through question retrieval and diversification. Inf Retrieval J 20, 575 (2017). https://doi.org/10.1007/s10791-017-9311-0

    Article  Google Scholar 

  6. Wang, M., Cao, D., Li, L., Li, S., Ji, R.: Microblog sentiment analysis based on cross-media bag-of-words model. In: Proceedings of International Conference on Internet Multimedia Computing and Service—ICIMCS’14, p. 76 (2014)

  7. Xue, B., Zhang, M., Browne, W.N.: Single feature ranking and binary particle swarm optimisation based feature subset ranking for feature selection. In: Proceedings of the 35th ACSC. Lecture Notes in Computer Science, vol. 122, pp. 27–36. Melbourne, Australia (2012)

  8. Wang, F., Yang, Y., Lv, X., Xu, J., Li, L.: Feature selection using feature ranking, correlation analysis and chaotic binary particle swarm optimization. In: 2014 IEEE 5th International Conference on Software Engineering and Service Science, pp. 305–309, (2014)

  9. Nyaung, D.E., Thein, T.L.L.: Feature-based summarizing and ranking from customer reviews. World Acad. Sci. Eng. Technol. Int. J. Comput. Electr. Autom. Control Inf. Eng. 9(3), 734–739 (2015)

    Google Scholar 

  10. Reyes-Sierra, M., Coello, C.C.: Multi-objective particle swarm optimizers: a survey of the state-of-the-art. Int. J. Comput. Intell. Res. 2(3), 287–308 (2006)

    MathSciNet  Google Scholar 

  11. Coello, C.A.C., Pulido, G.T., Lechuga, M.S.: Handling multiple objectives with particle swarm optimization. IEEE Trans. Evolut. Comput. 8(3), 256–279 (2004)

    Article  Google Scholar 

  12. Mostaghim. S, Teich J: Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO) In: 2003 IEEE Swarm Intelligence Symposium Proceedings, Indianapolis, Indiana, USA, IEEE Service Center, pp. 26–33 (2003)

  13. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi objective genetic algorithm: NSGA II. IEEE Trans. Evolut. Comput. 6, 182–197 (2002)

    Article  Google Scholar 

  14. Li, X.: A non-dominated sorting particle swarm optimizer for multi-objective optimization. In: Genetic and evolutionary computation- GECCO 2003. Proceedings, Part I. Lecture Notes in Computer Science, vol. 2723, pp. 37–48. Springer (2003)

  15. Li, X: Better spread and convergence: particle swarm multi objective optimization using the maximin fitness function. In: Proceedings of the 2004 Genetic and Evolutionary Computation Conference. Part 1. Lecture Notes in Computer Science, vol. 3102, pp. 117–128. Seattle, Washington, USA, Springer (2004)

  16. Binwahlan, M.S., Salim, N., Suanmali, L.: Swarm based features selection for text summarization. IJCSNS 9(1), 175–179 (2009)

    Google Scholar 

  17. Xue, B., Zhang, M., Browne, W.N.: Particle swarm optimization for feature selection in classification: a multi- objective Approach. IEEE Trans. Cybern. 10, 1–16 (2012)

    Google Scholar 

  18. Sierra, M.R., Coello, C.A.C.: Improving PSO-based multi-objective optimization using crowding, mutation and epsilon-dominance. In: Proceedings of EMO, pp. 505–519 (2005)

  19. Zhou, Z., Liu, X., Li, P., Shang, L.: Feature selection method with proportionate fitness based binary particle swarm optimization. In: Simulated evolution and learning, pp. 582–592. Springer, New York (2014)

  20. Xue, B., Zhang, M., Browne, W.N.: New fitness functions in binary particle swarm optimisation for feature selection. In: Proceedings of the IEEE CEC, pp. 1–8 (2012)

  21. Wang, X., Yang, J., Teng, X., Xia, W., Jensen, R.: Feature selection based on rough sets and particle swarm optimization. Pattern Recognit. Lett. 28(4), 459–471 (2007)

    Article  Google Scholar 

  22. Kumar, V., Minz, S.: Multi-objective particle swarm optimization: an introduction. SmartCR 4(5), 335–353 (2014)

    Article  Google Scholar 

  23. Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391 (1990)

    Article  Google Scholar 

  24. Khairnar, J., Kinikar, M.: Latent semantic analysis method used for mobile rating and review summarization. Int. J. Comput. Sci. Telecommun. 4(6), 61–67 (2013)

    Google Scholar 

  25. Somprasertsri, G., Lalitrojwong, P.: Mining feature-opinion in online customer reviews for opinion summarization. J. UCS 16(6), 938–955 (2010)

    Google Scholar 

  26. Engelbrecht, A.P.: Fundamentals of Computational Swarm Intelligence. Wiley, England (2005)

    Google Scholar 

  27. Thangaraj, R., Pant, M., Abraham, A.: A new diversity guided particle swarm optimization with mutation. In: World Congress on Nature & Biologically Inspired Computing (NaBIC), pp. 294–299 (2009)

  28. Lin, C.-Y. 2004 ROUGE: a package for automatic evaluation of summaries. In: Proceedings of the workshop on Text Summarization Branches Out(WAS 2004), Barcelona, Spain, July 25–26 (2004)

  29. Text Analytics 101, Evaluation metrics. http://textanalytics101.rxnlp.com/5/2017/01/how-rouge-works-for-evaluation-of.html

  30. Zitzler, Eckart, Deb, Kalyanmoy, Thiele, Lothar: Comparison of multi objective evolutionary algorithms: empirical results. Evolut. Computat. 8(2), 173–195 (2000)

    Article  Google Scholar 

  31. Durillo, J.J., García-Nieto, J., Nebro, A.J., Coello, C.A. C., Luna, F., Alba, E.: Multi-objective particle swarm optimizers: an experimental comparison. In: International Conference on Evolutionary Multi-Criterion Optimization, pp. 495–509, Springer, Hiedelberg,(2009)

  32. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. In: Proceedings of the EUROGEN2001 Conference, Barcelona, Spain, CIMNE, pp. 95–100 (2002)

  33. http://www.text-analytics101.com

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, Pollachi, 642002, India

    V. Priya

  2. Department of Information Technology, PSG College of Technology, Coimbatore, 642003, India

    K. Umamaheswari

Authors
  1. V. Priya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Umamaheswari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. Priya.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Priya, V., Umamaheswari, K. Enhanced continuous and discrete multi objective particle swarm optimization for text summarization. Cluster Comput 22 (Suppl 1), 229–240 (2019). https://doi.org/10.1007/s10586-018-2674-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2674-1

Keywords

Search

Navigation