Abstract
In this paper we present an approach to document summarization based on unsupervised techniques. We study the adequacy of these techniques to the problem of documents in which many topics of different duration are present, in our case the transcriptions of Spanish TV programs. The paper compares a classical Latent Semantic Analysis approach to a new proposal based on Latent Dirichlet Allocation. It is also studied the application of the summarization process to the different segments obtained in a previous process of topic segmentation. The topic segmentation is performed by considering distances between paragraphs, that are represented by means of continuous vectors obtained from the words contained in them. Experiments on some TV programs of political and miscellaneous news have been performed.
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
Similar content being viewed by others
Notes
References
Beeferman, D., Berger, A., Lafferty, J.: Statistical models for text segmentation. Mach. Learn. 34(1), 177–210 (1999)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003). http://dl.acm.org/citation.cfm?id=944919.944937
Cheng, J., Lapata, M.: Neural summarization by extracting sentences and words. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, 7–12 August 2016, Berlin, Volume 1: Long Papers (2016)
Erkan, G., Radev, D.R.: Lexrank: graph-based lexical centrality as salience in text summarization. J. Artif. Int. Res. 22(1), 457–479 (2004)
Fuentes, M., Alfonseca, E., Rodríguez, H.: Support vector machines for queryfocused summarization trained and evaluated on pyramid data. In: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, ACL 2007, pp. 57–60. Association for Computational Linguistics, Stroudsburg (2007). http://dl.acm.org/citation.cfm?id=1557769.1557788
Furui, S., Kikuchi, T., Shinnaka, Y., Hori, C.: Speech-to-text and speech-to-speech summarization of spontaneous speech. IEEE Trans. Speech Audio Process. 12(4), 401–408 (2004)
Gong, Y., Liu, X.: Generic text summarization using relevance measure and latent semantic analysis. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2001, pp. 19–25. ACM, New York (2001). https://doi.org/10.1145/383952.383955
Lin, C.Y.: Rouge: a package for automatic evaluation of summaries. In: MarieFrancine Moens, S.S. (ed.) Text Summarization Branches Out: Proceedings of the ACL-04 Workshop, pp. 74–81. Association for Computational Linguistics, Barcelona (2004)
Lloret, E., Palomar, M.: Text summarisation in progress: a literature review. Artif. Intell. Rev. 37(1), 1–41 (2012)
Mikolov, T., Chen, K., Corrado, G.S., Dean, J.: Efficient estimation of word representations in vector space. CoRR abs/1301.3781 (2013)
Nallapati, R., Zhai, F., Zhou, B.: Summarunner: a recurrent neural network based sequence model for extractive summarization of documents. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, , San Francisco, 4–9 February 2017, pp. 3075–3081 (2017)
Ozsoy, M.G., Cicekli, I., Alpaslan, F.N.: Text summarization of turkish texts using latent semantic analysis. In: Proceedings of the 23rd International Conference on Computational Linguistics, COLING 2010, pp. 869–876. Association for Computational Linguistics, Stroudsburg (2010). http://dl.acm.org/citation.cfm?id=1873781.1873879
Shen, D., Sun, J.T., Li, H., Yang, Q., Chen, Z.: Document summarization using conditional random fields. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence, IJCAI 2007, pp. 2862–2867 (2007)
Tur, G., De Mori, R.: Spoken Language Understanding: Systems for Extracting Semantic Information from Speech. Wiley, New York (2011)
Acknowledgments
This work has been partially supported by the Spanish MINECO and FEDER founds under project AMIC: Affective Multimedia Analytics with Inclusive and Natural Communication (TIN2017-85854-C4-2-R).
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
Canora, M., García-Granada, F., Sanchis, E., Segarra, E. (2018). An Approach to Automatic Summarization of Television Programs. In: Karpov, A., Jokisch, O., Potapova, R. (eds) Speech and Computer. SPECOM 2018. Lecture Notes in Computer Science(), vol 11096. Springer, Cham. https://doi.org/10.1007/978-3-319-99579-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-99579-3_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99578-6
Online ISBN: 978-3-319-99579-3
eBook Packages: Computer ScienceComputer Science (R0)