Abstract
The main problem for generating an extractive automatic text summary is to detect the most relevant information in the source document. Although, some approaches claim being domain and language independent, they use high dependence knowledge like key-phrases or golden samples for machine-learning approaches. In this work, we propose a language- and domain-independent automatic text summarization approach by sentence extraction using an unsupervised learning algorithm. Our hypothesis is that an unsupervised algorithm can help for clustering similar ideas (sentences). Then, for composing the summary, the most representative sentence is selected from each cluster. Several experiments in the standard DUC-2002 collection show that the proposed method obtains more favorable results than other approaches.
Work done under partial support of Mexican Government: CONACyT, SNI, SIP-IPN, PIFI-IPN.
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References
Lin, C.Y., Hovy, E.: Automated Text Summarization in SUMMARIST. In: Proc. of ACL Workshop on Intelligent, Scalable Text Summarization, Madrid, Spain (1997)
Song, Y., et al.: A Term Weighting Method based on Lexical Chain for Automatic Summarization. In: Gelbukh, A. (ed.) CICLing 2004. LNCS, vol. 2945, pp. 636–639. Springer, Heidelberg (2004)
HaCohen-Kerner, Y., Zuriel, G., Asaf, M.: Automatic Extraction and Learning of Keyphrases from Scientific Articles. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 657–669. Springer, Heidelberg (2005)
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.W., Yang, J.: Text Summarization by Sentence Segment Extraction Using Machine Learning Algorithms. In: Proc. of the ACL 2004 Workshop, Barcelona, España (2004)
Neto, L., Freitas, A.A., Kaestner, C.A.A.: Automatic Text Summarization using a Machine learning Approach. In: Proceedings of the ACL 2004 Workshop, Barcelona, España (2004)
Ledeneva, Y., Gelbukh, A., García, H.R.: Terms Derived from Frequent Sequences for Extractive Text Summarization. In: Gelbukh, A. (ed.) CICLing 2008. LNCS, vol. 4919, pp. 593–604. Springer, Heidelberg (2008)
Ledeneva, Y., Gelbukh, A., García, H.R.: Keeping Maximal Frequent Sequences Facilitates Extractive Summarization. Research in Computing Science 34 (2008)
Cristea, D., Postolache, O., Pistol, I.: Summarization through Discourse Structure. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 632–644. Springer, Heidelberg (2005)
Kupiec, J., Pedersen, J.O., Chen, F.: A Trainable Document Summarizer. In: Proc. 18th ACM-SIGIR Conf. on Research and Development in Information Retrieval, pp. 68–73 (1995)
DUC. Document Understanding Conference 2002 (2002), www-nlpir.nist.gov/projects/duc
Xu, W., Li, W., Wu, M., Li, W., Yuan, C.: Deriving Event Relevance from the Ontology Constructed with Formal Concept Analysis. In: Gelbukh, A. (ed.) CICLing 2006. LNCS, vol. 3878, pp. 480–489. Springer, Heidelberg (2006)
Mihalcea, R.: Random Walks on Text Structures. In: Gelbukh, A. (ed.) CICLing 2006. LNCS, vol. 3878, pp. 249–262. Springer, Heidelberg (2006)
Mihalcea, R., Tarau, P.: TextRank: Bringing Order into Texts. In: Proc. Empirical Methods in Natural Language Processing (EMNLP 2004), Barcelona, Spain (2004)
Hassan, S., Mihalcea, R., Banea, C.: Random-Walk Term Weighting for Improved Text Classification. In: Proc. Semantic Computing (ICSC 2007), Irvine, CA (2007)
Liu, D., He, Y., Ji, D., Hua, J.: Multi-Document Summarization Based on BE-Vector Clustering. In: Gelbukh, A. (ed.) CICLing 2006. LNCS, vol. 3878, pp. 470–479. Springer, Heidelberg (2006)
Bolshakov, I.A.: Getting One’s First Million...Collocations. In: Gelbukh, A. (ed.) CICLing 2004. LNCS, vol. 2945, pp. 229–242. Springer, Heidelberg (2004)
Koster, C.H.A.: Transducing Text to Multiword Units. In: Workshop on Multiword Units MEMURA at 4th Int. Conf. on Language Resources and Evaluation, LREC 2004, Portugal (2004)
Sidorov, G., Gelbukh, A.: Automatic Detection of Semantically Primitive Words Using Their Reachability in an Explanatory Dictionary. In: Proc. Int. Workshop on Natural Language Processing and Knowledge Engineering, NLPKE 2001, USA, pp. 1683–1687 (2001)
Luhn, H.P.: A Statical Approach to Mechanical Encoding and Searching of Literary Information. IBM Journal of Research and Development, 309–317 (1975)
Salton, G., Buckley, C.: Term-Weighting Approaches in Automatic Text Retrieval. Information Processing & Management 24, 513–523 (1988)
Lin, C.Y.: ROUGE: A Package for Automatic Evaluation of Summaries. In: Proceedings of Workshop on Text Summarization of ACL, Spain (2004)
Lin, C.Y., Hovy, E.: Automatic Evaluation of Summaries Using N-gram Co-Occurrence Statistics. In: Proceedings of HLT-NAACL, Canada (2003)
Spark Jones, K., Willet, P.: Reading in Information Retrieval. Morgan Kaufmann, San Francisco (1997)
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García-Hernández, R.A., Montiel, R., Ledeneva, Y., Rendón, E., Gelbukh, A., Cruz, R. (2008). Text Summarization by Sentence Extraction Using Unsupervised Learning. In: Gelbukh, A., Morales, E.F. (eds) MICAI 2008: Advances in Artificial Intelligence. MICAI 2008. Lecture Notes in Computer Science(), vol 5317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88636-5_12
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DOI: https://doi.org/10.1007/978-3-540-88636-5_12
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