Abstract
Extractive text summarization consists in selecting the most important units (normally sentences) from the original text, but it must be done as closer as humans do. Several interesting automatic approaches are proposed for this task, but some of them are focused on getting a better result rather than giving some assumptions about what humans use when producing a summary. In this research, not only the competitive results are obtained but also some assumptions are given about what humans tried to represent in a summary. To reach this objective a genetic algorithm is proposed with special emphasis on the fitness function which permits to contribute with some conclusions.
Chapter PDF
Similar content being viewed by others
References
Lee, J.-H., Park, S., Ahn, C.-M., Kim, D.: Automatic Generic Document Summarization Based on Non-negative Matrix Factorization. Information Processing and Management 45, 20–34 (2009)
Luhn, H.P.: The automatic creation of Literature abstracts. IBM Journal of Research and Development (1958)
Garcia-Hernandez, R.A., Montiel, R., Ledeneva, Y., Rendon, E., Gelbukh, A., Cruz, R.: Text Summarization by Sentence Extraction Using Unsupervised Learning. In: Orejas, F., Ehrig, H., Jantke, K.P., Reichel, H. (eds.) Abstract Data Types 1990. LNCS (LNAI), vol. 534, pp. 133–143. Springer, Heidelberg (1991)
Edmondson, H.P.: New Methods in Automatic Extraction. Journal of the Association for Computing Machinery (1969)
Kupiec, J., Pedersen, J., Chen, F.: A trainable document summarizer. In: SIGIR 1995 (1995)
Villatoro-Tello, E., Villaseñor-Pineda, L., Montes-y-Gómez, M.: Using Word Sequences for Text Summarization. In: Sojka, P., Kopeček, I., Pala, K. (eds.) TSD 2006. LNCS (LNAI), vol. 4188, pp. 293–300. Springer, Heidelberg (2006)
Chuang, T., Yang, J.: Text Summarization by Sentence Segment Extraction Using Machine Learning Algorithms. In: Proc. of the ACL 2004 Workshop, Barcelona, España (2004)
Ledeneva, Y.: PhD. Thesis: Automatic Language-Independent Detection of Multiword Descriptions for Text Summarization. National Polytechnic Institute, Mexico (2009)
Ledeneva, Y.N., Gelbukh, A., García-Hernández, R.A.: Terms Derived from Frequent Sequences for Extractive Text Summarization. In: Gelbukh, A. (ed.) CICLing 2008. LNCS, vol. 4919, pp. 593–604. Springer, Heidelberg (2008)
Garcia-Hernandez, R.A., Martinez-Trinidad, J.F., Carrasco, A.: Finding maximal sequential patterns in text document collections and single documents. Informatica. International Journal of Computing and Informatics (34), 93–101 (2010)
Ledeneva, Y., Garcia-Hernandez, R., Gelbukh, A.: Multi-document summarization using Maximal Frequent Sequences. Research in Computer Science 47, 15–24 (2010)
Garcia-Hernandez, R., Ledeneva, Y., Gelbukh, A., Citlalih, G.: An Assessment of Word Sequence Models for Extractive Text Summarization. Research in Computing Science (38), 253–262 (2008)
Suanmali, L., Salim, N., Salem Binwahlan, M.: Genetic Algorithm based Sentence Extraction for Text Summarization. International Journal of Innovative Computing 1(1) (2011)
Silla, C.N., Pappa, G.L., Freitas, A.A., Kaestner, C.A.A.: Automatic text summarization with genetic algorithm-based attribute selection. In: Lemaître, C., Reyes, C.A., González, J.A. (eds.) IBERAMIA 2004. LNCS (LNAI), vol. 3315, pp. 305–314. Springer, Heidelberg (2004)
Qazvinian, V., Sharif, L., Halavati, R.: Summarising text with a genetic algorithm-based sentence extraction. Int. J. Knowledge Management Studies 2(4), 426–444 (2008)
Cruz, C.M., Urrea, A.M.: Extractive Summarization Based on Word Information and Sentence Position. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 653–656. Springer, Heidelberg (2005)
Rada, M., Tarau, P.: TextRank: Bringing Order into Texts. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2004 (2004)
van Rijsbergen, C.J., Robertson, S.E., Porter, M.F.: New models in probabilistic information retrieval. En línea (1980) http://tartarus.org/~martin/PorterStemmer/index.html (Último acceso: Enero 28, 2013)
Document Understanding Conferences. En línea (Julio 16, 2002), http://www-nlpir.nist.gov/projects/duc/index.html2
Lin, C.Y.: ROUGE: A Package for Automatic Evaluation of Summaries. In: Proceedings of Workshop on Text Summarization of ACL (2004)
Lin, C., Hovy, E.: Automatic Evaluation of Summaries Using N-gram Co-Occurrence. In: Proceedings of HLT-NAACL, Canada, (2003)
Ledeneva, Y., Hernández, R.G., Soto, R.M., Reyes, R.C., Gelbukh, A.: EM Clustering Algorithm for Automatic Text Summarization. In: Batyrshin, I., Sidorov, G. (eds.) MICAI 2011, Part I. LNCS, vol. 7094, pp. 305–315. Springer, Heidelberg (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
García-Hernández, R.A., Ledeneva, Y. (2013). Single Extractive Text Summarization Based on a Genetic Algorithm. In: Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Rodríguez, J.S., di Baja, G.S. (eds) Pattern Recognition. MCPR 2013. Lecture Notes in Computer Science, vol 7914. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38989-4_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-38989-4_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38988-7
Online ISBN: 978-3-642-38989-4
eBook Packages: Computer ScienceComputer Science (R0)