Abstract
The goal of automated summarization is to tackle the “information overload” problem by extracting and perhaps compressing the most important content of a document. Due to the difficulty that single-document summarization has in beating a standard baseline, especially for news articles, most efforts are currently focused on multi-document summarization. The goal of this study is to reconsider the importance of single-document summarization by introducing a new approach and its implementation. This approach essentially combines syntactic, semantic, and statistical methodologies, and reflects psychological findings that pinpoint specific selection patterns as humans construct summaries. Successful summary evaluation results and baseline out-performance are demonstrated when our system is executed on two separate datasets: the Document Understanding Conference (DUC) 2002 data set and a scientific magazine article set. These results have implications not only for extractive and abstractive single-document summarization, but could also be leveraged in multi-document summarization.
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References
Angheluta, R., Mitra, R., Jing, X., Moens, M.-F.: K.U. Leuven Summarization System at DUC 2004. Available on the Web (2004)
Arora, R., Ravindran, B.: Latent Dirichlet Allocation and Singular Value Decomposition based Multi-Document Summarization. In: ICDM 2008: Proceedings of the 2008 Eighth IEEE Int’l Conf. on Data Mining, pp. 713–718 (2008)
Baxendale, P.: Machine-made Index for Technical Literature - An Experiment. IBM Journal of Research Development 2(4), 354–361 (1958)
Brin, S., Page, L.: The Anatomy of Large-scale Hypertextual Web Search Engine. Computer Networds and ISDN Systems 30, 1–7 (1998)
Edmundson, H.: New Methods in Automatic Extraction. Journal of ACM 16(2), 264–285 (1969)
Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database. MIT Press (1998)
Hovy, E., Lin, C.: Automatic Text Summarization in SUMMARIST. In: Mani, Maybury, M. (eds.) Adv. in Text Summarization, vol. 1. MIT Press (1999)
Ishikawa, K.: Trainable Automatic Text Summarization Using Segmentation of Sentence. In: Proceedings of the Third NTCIR Workshop (2003)
Li, S., Wang, W., Wang, C.: TAC 2009 Update Summarization Task of ICL. In: Text Analysis Conference 2008 (2008)
Lin, C.: ROUGE: A Package for Automatic Evaluation of Summaries. In: Proceedings of Workshop on Text Summarization Post-Conference Workshop (ACL 2004), Barcelona, Spain (2004)
Lin, C., Hovy, E.: Automatic Evaluation of Summaries Using n-gram Co-occurrence Statistics. In: HTL-NAACL (2003)
Lin, C.-Y., Hovy, E.H.: Identifying topics by position. In: ANLP, pp. 283–290 (1997)
Lorch, R., Lorch, E.: Effects of Headings of Text Recall and Summarization. Contemporary Educational Psychology 21, 261–278 (1996)
Mani, I., Maybury, M.: Advances in Automatic Summarization. MIT Press, Cambridge (1999)
Mihalcea, R., Ceylan, H.: Explorations in Automatic Book Summarization. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2007), Prague (2007)
Mihalcea, R., Tarau, P.: TextRank: Bringing Order into Texts. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP 2004 (March 2004)
Nenkova, A.: Automatic Text Summarization of Newswire: Lessons Learned from the document understanding conference. In: AAAI, pp. 1436–1441 (2005)
Nenkova, A.: A General Introduction to Automatic Summarization (2009), http://webcast.jhu.edu/mediasite/Viewer/?peid=8cd235b1699a457f9c776c12d4925408
Radev, D., Allison, T.: Mead - a Platform for Multidocument Multilingual Text Summarization. In: LREC (2004)
Radev, D., Jing, H., Stys, M., Tam, D.: Centroid-based Summarization of Multiple Documents. Information Proc. and Mgmt. 40, 919–938 (2004)
Svore, K.M., Vanderwende, L., Burges, C.J.C.: Enhancing Single-document Summarization by Combining RankNet and Third-Party Sources. In: EMNLP-CoNLL, pp. 448–457 (2007)
Verma, R., Filozov, F.: Document Map and WN-Sum: A new framework for automatic text summarization and a first implementation. Technical Report UH-CS-10-03, University of Houston Computer Science Dept. (2010)
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Barrera, A., Verma, R. (2012). Combining Syntax and Semantics for Automatic Extractive Single-Document Summarization. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2012. Lecture Notes in Computer Science, vol 7182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28601-8_31
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DOI: https://doi.org/10.1007/978-3-642-28601-8_31
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