Performance analysis of Modified Shuffled Frog leaping Algorithm for Multi-document Summarization Problem

Rasmita Rautray, Rasmita Dash, Rajashree Dash

Abstract


Due to massive growth of Web information, handling useful information has become a challenging issue in now-a-days.  In the past few decades, text summarization is considered as one of the solution to obtained relevant information from extensive collection of information. In this paper, a novel approach using modified shuffled frog leaping algorithm (MSFLA) to extract the important sentence from multiple documents is presented. The effectiveness of MSFLA algorithm for summarization model is evaluated by comparing the ROUGE score and statistical analysis of the model with respect to results of other summarization models. The models are demonstrated by the simulation results over DUC datasets. In the present work, it elucidates that MSFLA based model improves the results and find advisable solution for summary extraction

Full Text:

PDF

References


Rautray, R., & Balabantaray, R. C. (2017). An evolutionary framework for multi document summarization using Cuckoo search approach: MDSCSA. Applied Computing and Informatics.

H. P. Luhn, The automatic creation of literature abstracts, IBM Journal of Research and Development 2 (2) (1958) 159–165, doi:10.1147/rd.22.0159.

L. Wang, H. Raghavan, V. Castelli, R. Florian, C. Cardie, A sentence compression based framework to query-focused multi-document summarization, arXiv preprint arXiv:1606.07548.

R. Barzilay, K. R. McKeown, M. Elhadad, Information fusion in the context of multi-document summarization, in: Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics, Association for Computational Linguistics, 1999, pp.550–557.

K. R. McKeown, J. L. Klavans, V. Hatzivassiloglou, R. Barzilay, E. Eskin, Towards multidocument summarization by reformulation: Progress and prospects, In Proceedings of AAAI-99.

Sharma, T. K., & Pant, M. (2017). Opposition based learning ingrained shuffled frog-leaping algorithm. Journal of Computational Science, 21, 307-315.

Dalavi, A. M., Pawar, P. J., & Singh, T. P. (2016). Optimal sequence of hole-making operations using particle swarm optimization and modified shuffled frog leaping algorithm. Engineering Review, 36(2), 187-196.

Dash, R. (2017). An improved shuffled frog leaping algorithm based evolutionary framework for currency exchange rate prediction. Physica A: Statistical Mechanics and its Applications, 486, 782-796.

Kaur, P., & Mehta, S. (2017). Resource provisioning and work flow scheduling in clouds using augmented Shuffled Frog Leaping Algorithm. Journal of Parallel and Distributed Computing, 101, 41-50.

Amirian, H., & Sahraeian, R. (2017). Solving a grey project selection scheduling using a simulated shuffled frog leaping algorithm. Computers & Industrial Engineering, 107, 141-149.

Tang, D., Yang, J., Dong, S., & Liu, Z. (2016). A lévy flight-based shuffled frog-leaping algorithm and its applications for continuous optimization problems. Applied Soft Computing, 49, 641-662.

Sharma, S., Sharma, T. K., Pant, M., Rajpurohit, J., & Naruka, B. (2015). Centroid mutation embedded shuffled frog-leaping algorithm. Procedia Computer Science, 46, 127-134.

Bhattacharjee, K. K., & Sarmah, S. P. (2014). Shuffled frog leaping algorithm and its application to 0/1 knapsack problem. Applied Soft Computing, 19, 252-263.

Hasanien, H. M. (2015). Shuffled frog leaping algorithm for photovoltaic model identification. IEEE Transactions on Sustainable Energy, 6(2), 509-515.

Kaur, P., & Mehta, S. (2017). Resource provisioning and work flow scheduling in clouds using augmented Shuffled Frog Leaping Algorithm. Journal of Parallel and Distributed Computing, 101, 41-50.

Huynh, T. H. (2008, April). A modified shuffled frog leaping algorithm for optimal tuning of multivariable PID controllers. In Industrial Technology, 2008. ICIT 2008. IEEE International Conference on (pp. 1-6). IEEE.

Zhang, X., Hu, X., Cui, G., Wang, Y., & Niu, Y. (2008, June). An improved shuffled frog leaping algorithm with cognitive behavior. In Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on (pp. 6197-6202). IEEE.

Pu, H., Zhen, Z., & Wang, D. (2011). Modified shuffled frog leaping algorithm for optimization of UAV flight controller. International Journal of Intelligent Computing and Cybernetics, 4(1), 25-39.

Chittineni, S., Godavarthi, D., Pradeep, A. N. S., Satapathy, S. C., & Reddy, P. P. (2011, July). A modified and efficient shuffled frog leaping algorithm (MSFLA) for unsupervised data clustering. In International Conference on Advances in Computing and Communications (pp. 543-551). Springer, Berlin, Heidelberg.

Liang, B., Zhen, Z., & Jiang, J. (2016). Modified shuffled frog leaping algorithm optimized control for air-breathing hypersonic flight vehicle. International Journal of Advanced Robotic Systems, 13(6), 1729881416678136.

Sabbah, T., Selamat, A., Selamat, M. H., Al-Anzi, F. S., Viedma, E. H., Krejcar, O., & Fujita, H. (2017). Modified frequency-based term weighting schemes for text classification. Applied Soft Computing, 58, 193-206.

http://duc.nist.gov

C. Y. Lin, E. Hovy, 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 (pp. 71-78). Association for Computational Linguistics.

Rautray, R., & Balabantaray, R. C. (2017). Cat swarm optimization based evolutionary framework for multi document summarization. Physica A: Statistical Mechanics and its Applications, 477, 174-186.

Hollander, M., & Wolfe, D. A. (1999). Nonparametric statistical methods (2nd ed.). Wiley-Interscience (p.787).




DOI: https://doi.org/10.31449/inf.v43i3.2310

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.