Text Summary using Modified Particle Swarm Optimization Algorithm


  • V. S. Raj Kumar
  • R. Danu
  • S. Shanmugapriya
  • R. Vinod


Differential Evolution Algorithm, Modified Particle Swarm Optimization Algorithm


Text summarization is the process of provides meaningful and short contents of the documents in the automated manner by using which concept of entire documents can be found. In the existing work, an approach called as unsupervised generic summary creation is introduced to summarize the single document and as well as multiple documents in the generic manner. However, the genetic algorithm will consume more computational time for generating the summaries in case of presence of multiple documents with more sentences. This problem is resolved in the proposed approach by introducing the modified particle swarm optimization where global best would be updated based on weighted mean approach. This proposed approach provides an efficient and flexible creation of summaries with reduced computation time. The experimental tests conducted were proves that the proposed approach provides better result than the existing approach in terms of reduced computational time.


Rasim M. Alguliyev, Ramiz M. Aliguliyev, Nijat R. Isazade (2015), “An unsupervised approach to generating generic summaries of documentsâ€, Applied Soft Computing 34236–250.

Maolong Xi, Jun Sun, WenboXu (2008), “An improved quantum-behaved particle swarm optimization algorithm with weighted mean best positionâ€, Applied Mathematics and Computation 205, 751–759.

S.A.Babar, Pallavi D.Patil (2014), “Improving Performance of Text Summarizationâ€, International Conference on Information and Communication Technologies (ICICT).

Y. Ouyang, W. Li, S. Li, Q. Lu (2011), “Applying regression models to query-focused multi-document summarizationâ€, Information Processing and Management 47 (2) 227–237.

J. Tang, L. Yao, D. Chen (2009), “Multi-topic based query-oriented summarizationâ€, in: Proceedings of the 9th SIAM International Conference on Data Mining, Nevada, USA, pp. 1148–1159.

Rasim Alguliev, Ramiz Aliguliyev, “Evolutionary algorithm for extractive text summarizationâ€, Intelligent Information Management, 2009, 1, 128-138.

S.Prabha, Dr.K.Duraiswamy, B.Priyanga, “Context-Based Similarity Analysis for Document Summarizationâ€, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 3, Issue 4, April 2014.

Sunita Sarkar , Arindam Roy and B. S. Purkayastha, “A Comparative Analysis of Particle Swarm Optimization and K-means Algorithm For Text Clustering Using Nepali WordNetâ€, International Journal on Natural Language Computing (IJNLC), Vol. 3, No.3, June 2014.

Chen Li, Yang Liu, Fei Liu, Lin Zhao, Fuliang Weng, “Improving Multi-documents Summarization by Sentence Compression based on Expanded Constituent Parse Trees†, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 691–701, October 25-29, 2014.




How to Cite

Raj Kumar, V. S., Danu, R., Shanmugapriya, S., & Vinod, R. (2017). Text Summary using Modified Particle Swarm Optimization Algorithm. Asian Journal of Applied Sciences, 4(6). Retrieved from https://ajouronline.com/index.php/AJAS/article/view/4360