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The Scientific World Journal
Volume 2016, Article ID 1784827, 10 pages
http://dx.doi.org/10.1155/2016/1784827
Research Article

An Automatic Multidocument Text Summarization Approach Based on Naïve Bayesian Classifier Using Timestamp Strategy

1Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Pennalur, Sriperumbudur TK 602117, India
2Department of Information Technology, RMK Engineering College, Kavaraipettai 601206, India

Received 20 October 2015; Revised 5 January 2016; Accepted 13 January 2016

Academic Editor: Juan M. Corchado

Copyright © 2016 Nedunchelian Ramanujam and Manivannan Kaliappan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. Radev, T. Allison, S. Blair-Goldensohn et al. et al., “MEAD—a platform for multidocument multilingual text summarization,” in Proceedings of the Conference on Language Resources and Evaluation (LREC '04), Lisbon, Portugal, May 2004.
  2. Y. Ma and J. Wu, “Combining N-gam and dependency word pair for multi-document Summarization,” in Proceedings of the IEEE 17th International Conference on Computational Science and Engineering (CSE '14), pp. 27–31, Chengdu, China, December 2014. View at Publisher · View at Google Scholar
  3. R. M. Alguliev, R. M. Aliguliyev, and N. R. Isazade, “Multiple documents summarization based on evolutionary optimization algorithm,” Expert Systems with Applications, vol. 40, no. 5, pp. 1675–1689, 2013. View at Publisher · View at Google Scholar · View at Scopus
  4. R. M. Alguliev, R. M. Aliguliyev, and N. R. Isazade, “CDDS: constraint-driven document summarization models,” Expert Systems with Applications, vol. 40, no. 2, pp. 458–465, 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. B. Baruque and E. Corchado, “A weighted voting summarization of SOM ensembles,” Data Mining and Knowledge Discovery, vol. 21, no. 3, pp. 398–426, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  6. S. Xiong and Y. Luo, “A new approach for multi-document summarization based on latent semantic analysis,” in Proceedings of the Seventh International Symposium on Computational Intelligence and Design (ISCID '14), vol. 1, pp. 177–180, Hangzhou, China, December 2014. View at Publisher · View at Google Scholar
  7. Y. Su and W. Xiaojun, “SRRank: leveraging semantic roles for extractive multi-document summarization,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 22, pp. 2048–2058, 2014. View at Google Scholar
  8. G. Yang, D. Wen, Kinshuk, N.-S. Chen, and E. Sutinen, “A novel contextual topic model for multi-document summarization,” Expert Systems with Applications, vol. 42, no. 3, pp. 1340–1352, 2015. View at Publisher · View at Google Scholar · View at Scopus
  9. J.-U. Heu, I. Qasim, and D.-H. Lee, “FoDoSu: multi-document summarization exploiting semantic analysis based on social Folksonomy,” Information Processing & Management, vol. 51, no. 1, pp. 212–225, 2015. View at Publisher · View at Google Scholar · View at Scopus
  10. G. Glavaš and J. Šnajder, “Event graphs for information retrieval and multi-document summarization,” Expert Systems with Applications, vol. 41, no. 15, pp. 6904–6916, 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. R. Ferreira, L. de Souza Cabral, F. Freitas et al., “A multi-document summarization system based on statistics and linguistic treatment,” Expert Systems with Applications, vol. 41, no. 13, pp. 5780–5787, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. Y. K. Meena and D. Gopalani, “Domain independent framework for automatic text summarization,” Procedia Computer Science, vol. 48, pp. 722–727, 2015. View at Publisher · View at Google Scholar
  13. Y. Sankarasubramaniam, K. Ramanathan, and S. Ghosh, “Text summarization using Wikipedia,” Information Processing & Management, vol. 50, no. 3, pp. 443–461, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. A. Khan, N. Salim, and Y. Jaya Kumar, “A framework for multi-document abstractive summarization based on semantic role labelling,” Applied Soft Computing, vol. 30, pp. 737–747, 2015. View at Publisher · View at Google Scholar
  15. G. Erkan and D. R. Radev, “LexPageRank: prestige in multi-document text summarization,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP '04), pp. 365–371, Barcelona, Spain, July 2004.
  16. S. Saraswathi and R. Arti, “Multi-document text summarization using clustering techniques and lexical chaining,” ICTACT Journal on Soft Computing, vol. 1, no. 1, pp. 23–29, 2010. View at Google Scholar
  17. H.-T. Zheng, S.-Q. Gong, J.-M. Guo, and W.-Z. Wu, “Exploiting conceptual relations of sentences for multi-document summarization,” in Web-Age Information Management, vol. 9098 of Lecture Notes in Computer Science, pp. 506–510, Springer, Basel, Switzerland, 2015. View at Publisher · View at Google Scholar
  18. A. Celikyilmaz and D. Hakkani-Tür, “Discovery of topically coherent sentences for extractive summarization,” in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, Portland, Ore, USA, 2011.
  19. Y.-F. B. Wu, Q. Li, R. S. Bot, and X. Chen, “Domain-specific keyphrase extraction,” in Proceedings of the 14th ACM International Conference on Information and Knowledge Management (CIKM '05), pp. 283–284, November 2005. View at Scopus