Abstract
This paper evaluates the impact of a set of statistical and semantic features as applied to the task of extractive summary generation for English. This set includes word frequency, inverse sentence frequency, inverse term frequency, corpus-tailored stopwords, word senses, resolved anaphora and textual entailment. The obtained results show that not all of the selected features equally benefit the performance. The term frequency combined with stopwords filtering is a highly competitive baseline that nevertheless can be topped when semantic information is included. However, in the selected experiment environment the recall values improved less than expected and we are interested in further investigating the reasons.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Blake, C.: A Comparison of Document, Sentence, and Term Event Spaces. In: ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, pp. 601–608. ACL, Stroudsburg (2006)
Chuang, W.T., Yang, J.: Text Summarization by Sentence Segment Extraction Using Machine Learning Algorithms. In: Terano, T., Chen, A.L.P. (eds.) PAKDD 2000. LNCS, vol. 1805, pp. 454–457. Springer, Heidelberg (2000)
Fujii, Y., Kitaoka, N., Nakagawa, S.: Automatic Extraction of Cue Phrases for Important Sentences in Lecture Speech and Automatic Lecture Speech Summarization. In: INTERSPEECH, pp. 2801–2804 (2007)
Ferrández, O., Micol, M., Muñoz, R., Palomar, M.: A perspective-based approach for solving textual entailment recognition. In: ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, pp. 66–71 (2007)
Harabagiu, S., Lacatusu, F.: Topic Themes for Multi-document Summarization. In: 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 202–209 (2005)
Harabagiu, S., Hickl, A., Lacatusu, F.: Satisfying Information Needs with Multi-document Summaries. Information Processing & Management 43(6), 1619–1642 (2007)
Lin, C.Y.: ROUGE: A Package for Automatic Evaluation of Summaries. In: ACL Text Summarization Workshop, pp. 74–81 (2004)
Lloret, E., Ferrández, O., Muñoz, R., Palomar, M.: A Text Summarization Approach Under the Influence of Textual Entailment. In: 5th International Workshop on NLPCS, pp. 22–31 (2008)
Luhn, H.P.: The Automatic Creation of Literature Abstracts. IBM Journal of Research and Development 2(2), 157–165 (1958)
Nenkova, A., Vanderwende, L., McKeown, K.: A Compositional Context Sensitive Multi-doscument Summarizer: Exploring the Factors That Influence Summarization. In: 29th SIGIR, pp. 573–580. ACM, New York (2006)
Plaza, L., Díaz, A.: Using Semantic Graphs and Word Sense Disambiguation. Techniques to Improve Text Summarization. Procesamiento del Lenguaje Natural 47, 97–105 (2011)
Saggion, H.: A Robust and Adaptable Summarization Tool. Traitement Automatique des Languages 49(2), 103–125 (2008)
Steinberger, J., Poesio, M., Kabadjov, M.A., Ježek, K.: Two Uses of Anaphora Resolution in Summarization. Information Processing and Management 43(6), 1663–1680 (2007)
Tatar, D., Tamaianu-Morita, E., Mihis, A., Lupsa, D.: Summarization by Logic Segmentation and Text Entailment. In: 33rd CICLing, pp. 15–26 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vodolazova, T., Lloret, E., Muñoz, R., Palomar, M. (2012). A Comparative Study of the Impact of Statistical and Semantic Features in the Framework of Extractive Text Summarization. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2012. Lecture Notes in Computer Science(), vol 7499. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32790-2_37
Download citation
DOI: https://doi.org/10.1007/978-3-642-32790-2_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32789-6
Online ISBN: 978-3-642-32790-2
eBook Packages: Computer ScienceComputer Science (R0)