Abstract
The results of an extractive automatic summarization task depends to a great extend on the nature of the processed texts (e.g., news, medicine, or literature). In fact, general-purpose methods usually need to be adhoc modified to improve their performance when dealing with a particular application context. However, this customization requires a lot of effort from domain experts and application developers, which makes it not always possible nor appropriate. In this paper, we propose a multi-language approach to extractive summarization which adapts itself to different text domains in order to improve its performance. In a training step, our approach leverages the features of the text documents in order to classify them by using machine learning techniques. Then, once the text typology of each text is identified, it tunes the different parameters of the extraction mechanism solving an optimization problem for each of the text document classes. This classifier along with the learned optimizations associated with each document class allows our system to adapt to each of the input texts automatically. The proposed method has been applied in a real environment of a media company with promising results.
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
Notes
- 1.
In fact, they belong to ValF family of functions as well, but for the sake’s of readability we have decided to change their name.
- 2.
We are aware we could get rid of the baseline term, but it is useful for the sake of comparing our approach with generic approaches.
- 3.
- 4.
Stop words are common words without relevant information (e.g. articles or conjunctions).
- 5.
A lemma is the canonical form of a word. For example, in English, sing, sings, sang, sung, and singing are different forms of the same verb, with “sing” as their common lemma.
- 6.
- 7.
- 8.
- 9.
References
Brandow, R., Mitze, K., Rau, L.F.: Automatic condensation of electronic publications by sentence selection. Inf. Process. Manag. 31(5), 675–685 (1995)
Liu, Y., Li, S., Cao, Y., Lin, C.-Y., Han, D., Yu, Y.: Understanding and summarizing answers in community-based question answering services. In: Proceedings of the 22nd International Conference on Computational Linguistics (COLING 2008), pp. 497–504. Association for Computational Linguistics (2008)
Padhy, N., Mishra, P., Panigrahi, R.: The survey of data mining applications and feature scope. Int. J. Comput. Sci. Eng. Inf. Technol. 2(3), 43–58 (2012)
Gupta, V., Lehal, G.S.: A survey of text summarization extractive techniques. J. Emerg. Technol. Web Intell. 2(3), 258–268 (2010)
Lin, C.-Y., Hovy, E.: Automatic evaluation of summaries using n-gram co-occurrence statistics. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology (NAACL 2003), pp. 71–78. Association for Computational Linguistics (2003)
Lal, P., Ruger, S.: Extract-based summarization with simplification. In: Proceedings of the 2002 Workshop on Text Summarization (DUC 2002), pp. 1–8, NIST (2002)
Li, W., Wu, M., Lu, Q., Xu, W., Yuan, C.: Extractive summarization using inter-and intra-event relevance. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics (COLING ACL 2006), pp. 369–376. Association for Computational Linguistics (2006)
Nenkova, A., McKeown, K.: A survey of text summarization techniques. In: Mining Text Data, pp. 43–76. Springer, Boston (2012)
Luhn, H.P.: The automatic creation of literature abstracts. IBM J. Res. Dev. 2(2), 159–165 (1958)
Edmundson, H.P.: New methods in automatic extracting. J. ACM (JACM) 16(2), 264–285 (1969)
Kupiec, J., Pedersen, J., Chen, F.: A trainable document summarizer. In: Proceedings of the 18th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 1995), pp. 68–73. ACM (1995)
Lin, C.-Y.: Training a selection function for extraction. In: Proceedings of the 8th International Conference on Information and Knowledge Management (CIKM 1999), pp. 55–62. ACM (1999)
Conroy, J.M., O’leary, D.P.: Text summarization via hidden Markov models. In: Proceedings of the 24th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2001), pp. 406–407. ACM (2001)
Osborne, M.: Using maximum entropy for sentence extraction. In: Proceedings of the ACL-02 Workshop on Automatic Summarization (AS 2002), pp. 1–8. Association for Computational Linguistics (2002)
Svore, K.M., Vanderwende, L., Burges, C.J.: Enhancing single-document summarization by combining ranknet and third-party sources. In: Proceedings of the 2007 Joing Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL 2007), pp. 448–457. Association for Computational Linguistics (2007)
Ferreira, R., Freitas, F., de Souza Cabral, L., Lins, R.D., Lima, R., Franca, G., Simske, S.J., Favaro, L.: A context based text summarization system. In: Proceedings of the 11th IAPR International Workshop on Document Analysis Systems (DAS 2014), pp. 66–70. IEEE Xplore (2014)
Chang, Y., Wang, X., Mei, Q., Liu, Y.: Towards Twitter context summarization with user influence models. In: Proceedings of the 6th ACM International Conference on Web Search and Data Mining (WSDM 2013), pp. 527–536. ACM (2013)
Hwang, C.-L., Yoon, K.: Multiple attribute decision making: methods and applications a state-of-the-art survey, vol. 186. Springer Science & Business Media (2012)
Bond, F.F.: An Introduction to Journalism: A Survey of the Fourth Estate in all its Forms. Macmillan, New York (1954)
MacQuail, D.: Mass Communication Theory: An Introduction. Sage Publications, London (1983)
Wolny-Zmorzyński, K., Kozieł, A.: Journalistic genology. Media Stud. 54, 1–16 (2013)
Bell, A.: The discourse structure of news stories. In: Approaches to Media Discourse, pp. 64–104 (1998)
Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0026683
Shin, K.-S., Lee, T.S., Jung Kim, H.: An application of support vector machines in bankruptcy prediction model. Expert. Syst. Appl. 28(1), 127–135 (2005)
Garrido, A.L., Gomez, O., Ilarri, S., Mena, E.: NASS: news annotation semantic system. In: Proceedings of the 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2011), pp. 904–905. IEEE (2011)
Garrido, A.L., Gómez, O., Ilarri, S., Mena, E.: An experience developing a semantic annotation system in a media group. In: Bouma, G., Ittoo, A., Métais, E., Wortmann, H. (eds.) NLDB 2012. LNCS, vol. 7337, pp. 333–338. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31178-9_43
Garrido, A.L., Buey, M.G., Ilarri, S., Mena, E.: GEO-NASS: a semantic tagging experience from geographical data on the media. In: Catania, B., Guerrini, G., Pokorný, J. (eds.) ADBIS 2013. LNCS, vol. 8133, pp. 56–69. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40683-6_5
Garrido, A.L., Buey, M.G., Escudero, S., Peiro, A., Ilarri, S., Mena, E.: The GENIE project-a semantic pipeline for automatic document categorisation. In: Proceedings of the 10th International Conference on Web Information Systems and Technologies (WEBIST 2014), pp. 161–171, SCITEPRESS (2014)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988)
Garrido, A.L., Peiro, A., Ilarri, S.: Hypatia: An expert system proposal for documentation departments. In: Proceedings of the 12th International Symposium on Intelligent Systems and Informatics (SISY 2014), pp. 315–320. IEEE (2014)
Garrido, A.L., Ilarri, S., Sangiao, S., Gañan, A., Bean, A., Cardiel, O.: NEREA: named entity recognition and disambiguation exploiting local document repositories. In: Proceedings of the 28th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2016), pp. 1035–1042. IEEE (2016)
Acknowledgments
This research work has been supported by the CICYT project TIN2013-46238-C4-4-R, TIN2016-78011-C4-3-R (AEI/FEDER, UE), and DGA/FEDER. We want to thank Grupo Heraldo for their collaboration, and specially to Domingo Tardos and Susana Sangiao.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Garrido, A.L., Bobed, C., Cardiel, O., Aleyxendri, A., Quilez, R. (2018). Optimization in Extractive Summarization Processes Through Automatic Classification. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10762. Springer, Cham. https://doi.org/10.1007/978-3-319-77116-8_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-77116-8_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77115-1
Online ISBN: 978-3-319-77116-8
eBook Packages: Computer ScienceComputer Science (R0)