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Applied Computational Intelligence and Soft Computing
Volume 2013 (2013), Article ID 945623, 11 pages
Use of Genetic Algorithm for Cohesive Summary Extraction to Assist Reading Difficulties
Department of Computer Applications, National Institute of Technology, Tiruchirappalli 620015, India
Received 4 March 2013; Revised 30 April 2013; Accepted 23 May 2013
Academic Editor: Baoding Liu
Copyright © 2013 K. Nandhini and S. R. Balasundaram. 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.
- International Dyslexia Association, “Definition of dyslexia,” Based in the initial definition of the Research Committee of the Orton Dyslexia Society, former name of the IDA, done in, 2011, 1994, http://interdys.org/dyslexiadefinition.htm.
- M. Gajria, A. K. Jitendra, S. Sood, and G. Sacks, “Improving comprehension of expository text in students with LD: A research synthesis,” Journal of Learning Disabilities, vol. 40, no. 3, pp. 210–225, 2007.
- B. B. Armbruster, T. H. Anderson, and J. Ostertag, “Does text structure/summarization instruction facilitate learning from expository text?” Reading Research Quarterly, vol. 22, no. 3, pp. 331–346, 1987.
- A. S. Palinscar and A. L. Brown, “Reciprocal teaching of comprehensionfostering and comprehension-monitoring activities,” Cognition and Instruction, vol. 1, no. 2, pp. 117–175, 1984.
- National Reading Panel, Teaching Children to Read: An Evidence-Based Assessment of the Scientific Research Literature on Reading and Its Implications for Reading Instruction, National Institute of Child Health and Human Development, National Institutes of Health, 2000.
- M. L. Kamil, “Vocabulary and comprehension instruction: summary and implications of the national reading panel findings,” The Voice of Evidence in Reading Research, pp. 213–234, 2004.
- A. P. Sweet and C. E. Snow, Rethinking Reading Comprehension, Guilford Press, 2003.
- D. R. Reutzel, J. A. Smith, and P. C. Fawson, “An evaluation of two approaches for teaching reading comprehension strategies in the primary years using science information texts,” Early Childhood Research Quarterly, vol. 20, no. 3, pp. 276–305, 2005.
- P. A. Carpenter and M. Daneman, “Lexical retrieval and error recovery in reading: A model based on eye fixations,” Journal of Verbal Learning and Verbal Behavior, vol. 20, no. 2, pp. 137–160, 1981.
- R. D. Davis and E. M. Braun, The Gift of Dyslexia: Why Some of the Smartest People Can't Read... and How They Can Learn, Penguin Group, 2010.
- I. Mani and M. T. Maybury, Advances in Automatic Text Summarization, MIT press, 1999.
- I. L. Beck, M. G. McKeown, G. M. Sinatra, and J. A. Loxterman, “Revising social studies text from a text-processing perspective: evidence of improved comprehensibility,” Reading Research Quarterly, vol. 26, no. 3, pp. 251–276, 1991.
- D. S. McNamara, “Reading both high-coherence and low-coherence texts: effects of text sequence and prior knowledge,” Canadian Journal of Experimental Psychology, vol. 55, no. 1, pp. 51–62, 2001.
- J. H. Holland, “Genetic algorithms,” Scientific American, vol. 267, no. 1, pp. 66–72, 1992.
- L. G. Caldas and L. K. Norford, “A design optimization tool based on a genetic algorithm,” Automation in Construction, vol. 11, no. 2, pp. 173–184, 2002.
- H. Vafaie and K. De Jong, “Genetic algorithms as a tool for feature selection in machine learning,” in Proceedings of the 4th International Conference on Tools with Artificial Intelligence (TAI '92), pp. 200–203, 1992.
- C. N. Silla Jr., G. L. Pappa, A. A. Freitas, and C. A. A. Kaestner, “Automatic text summarization with genetic algorithm-based attribute selection,” in Proceedings of the 9th Ibero-American Conference on Advances in Artificial Intelligence (IBERAMIA '04), pp. 305–314, Springer, November 2004.
- L. Suanmali, N. Salim, and M. S. Binwahlan, “Genetic algorithm based sentence extraction for text summarization,” International Journal of Innovative Computing, vol. 1, no. 1, pp. 1–22, 2011.
- M. Litvak, M. Last, and M. Friedman, “A new approach to improving multilingual summarization using a genetic algorithm,” in Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL '10), pp. 927–936, Association for Computational Linguistics, July 2010.
- J.-Y. Yeh, H.-R. Ke, W.-P. Yang, and I.-H. Meng, “Text summarization using a trainable summarizer and latent semantic analysis,” Information Processing & Management, vol. 41, no. 1, pp. 75–95, 2005.
- M. A. Fattah and F. Ren, “GA, MR, FFNN, PNN and GMM based models for automatic text summarization,” Computer Speech and Language, vol. 23, no. 1, pp. 126–144, 2009.
- J. K. Fatma, M. Jaoua, L. H. Belguith, and A. B. Hamadou, “Experimentation of two compression strategies for multi-document summarization,” in Proceedings of the International Conference on Computer and Electrical Engineering (ICCEE '09), pp. 480–484, December 2009.
- D. Liu, Y. He, D. Ji, and H. Yang, “Genetic algorithm based multi-document summarization,” in PRICAI 2006: Trends in Artificial Intelligence, vol. 4099 of Lecture Notes in Computer Science, pp. 1140–1144, Springer, 2006.
- V. Qazvinian, L. Sharif, and R. Halavati, “Summarizing text with a genetic algorithm-based sentence extraction,” IJKMS, vol. 4, no. 2, pp. 426–444, 2008.
- C. Smith, H. Danielsson, and A. Jönsson, “Cohesion in automatically created summaries,” in Proceedings of the 4th Swedish Language Technology Conference, Lund, Sweden, 2012.
- J. Morris and G. Hirst, “Lexical cohesion computed by thesaural relations as an indicator of the structure of text,” Computational Linguistics, vol. 17, no. 1, pp. 21–48, 1991.
- I. Mani and E. Bloedorn, “Machine learning of generic and user-focused summarization,” in Proceedings of the 15th National Conference on Artificial Intelligence (AAAI '98), pp. 821–826, John Wiley & Sons, July 1998.
- H. G. Silber and K. F. McCoy, “Efficient text summarization using lexical chains,” in Proceedings of the 5th International Conference on Intelligent User Interfaces (IUI '00), pp. 252–255, ACM, January 2000.
- R. Barzilay and M. Elhadad, “Using lexical chains for text summarization,” in Proceedings of the ACL Workshop on Intelligent Scalable Text Summarization, vol. 17, pp. 10–17, Madrid, Spain, 1997.
- K. Nandhini and S. R. Balasundaram, “Significance of learner dependent features for improving text readability using extractive summarization,” in Proceedings of IEEE International Conference on Intelligent Human Computer Interaction (IHCI '12), 2012.
- N. Kumaresh and B. S. Ramakrishnan, “Graph based single document summarization,” in Data Engineering and Management, vol. 6411 of Lecture Notes in Computer Science, pp. 32–35, Springer, 2012.
- B. Jann, “Making regression tables from stored estimates,” Stata Journal, vol. 5, no. 3, pp. 288–308, 2005.
- D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, 1989.
- R. Gunning, “The fog index after twenty years,” Journal of Business Communication, vol. 6, no. 2, pp. 3–13, 1969.
- G. H. McLaughlin, “Smog grading-a new readability formula,” Journal of Reading, vol. 12, no. 8, pp. 639–646, 1969.
- J. P. Kincaid, L. R. P. Fishburne Jr., R. L. Rogers, and B. S. Chissom, “Derivation of new readability formulas (automated readability index, fog count and esch reading ease formula) for navy enlisted personnel,” 1975.
- J. S. Richardson, R. F. Morgan, and C. E. Fleener, Reading to Learn in the Content Areas, Wadsworth Publishing, 2011.
- M. A. K. Halliday and R. Hasan, Cohesion in english (english language), 1976.
- R. H. Maki and S. L. Berry, “Metacomprehension of text material,” Journal of Experimental Psychology: Learning, Memory, and Cognition, vol. 10, no. 4, pp. 663–679, 1984.