Abstract
Existing Web personalized information systems typically send to the users the title and the first lines of the chosen items, and links to the full text. This is, in most cases, insufficient for a user to detect if the item is relevant or not. An interesting approach is to replace the first sentences by a personalized summary extracted according to a user profile that represents the information needs of the user. On the other side, it is crucial to measure how much information is lost during the summarization process, and how this information loss may affect the ability of the user to judge the relevance of a given document. The system-oriented evaluation developed in this paper indicates that personalized summaries perform better than generic summaries in terms of identifying documents that satisfy user preferences. We also considered a user-centred qualitative evaluation indicating a high level of user satisfaction with the summarization method described, in consonance with the quantitative results.
This research has been partially funded by the Ministerio de Ciencia y Tecnología (TIC2002- 01961).
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References
Billsus, D., Pazzani, M.J.: User Modeling for Adaptive News Access. User Modeling and User-Adapted Interaction Journal 10(2-3), 147–180 (2000)
Díaz, A., Gervás, P.: Adaptive User Modeling for Personalization of Web Contents. In: De Bra, P.M.E., Nejdl, W. (eds.) AH 2004. LNCS, vol. 3137, pp. 65–74. Springer, Heidelberg (2004)
Edmundson, H.: New methods in automatic abstracting. Journal of the ACM 2(16), 264–285 (1969)
Kupiec, J., Pedersen, O., Chen, F.: A trainable document summarizer. Research and Development in Information Retrieval, 68–73 (1995)
Labrou, Y., Finin, T.: Yahoo! As an Ontology: Using Yahoo! Categories to Describe Documents. In: Proceedings of the 8th International Conference on Information Knowledgement (CIKM 1999), pp. 180–187. ACM Press, New York (2000)
Mani, I., Maybury, M.: Advances in Automatic Text Summarization. The MIT Press, Cambridge (1999)
Maña, M., Buenaga, M., Gómez, J.M.: Using and evaluating user directed summaries to improve information access. In: Abiteboul, S., Vercoustre, A.-M. (eds.) ECDL 1999. LNCS, vol. 1696, pp. 198–214. Springer, Heidelberg (1999)
Mizarro, S., Tasso, C.: Ephemeral and persistent personalization in adaptive information access to scholarly publications on the web. In: De Bra, P., Brusilovsky, P., Conejo, R. (eds.) AH 2002. LNCS, vol. 2347, pp. 306–316. Springer, Heidelberg (2002)
Salton, G.: Automatic Text Processing: The Transformation, Analysis and Retrieval of Information by Computer. Addison-Wesley Publishing, Reading (1989)
Teufel, S., Moens, M.: Sentence extraction as a classification task. In: Proceedings of ACL/EACL Workshop on Intelligent Scalable Text Summarization, Madrid, Spain, pp. 58–65 (1997)
Tombros, A., Sanderson, M.: Advantages of query-biased Summaries in IR. In: Proceedings of the 21st ACM SIGIR Conference, pp. 2–10 (1998)
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Díaz, A., Gervás, P., García, A. (2005). Evaluation of a System for Personalized Summarization of Web Contents. In: Ardissono, L., Brna, P., Mitrovic, A. (eds) User Modeling 2005. UM 2005. Lecture Notes in Computer Science(), vol 3538. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527886_63
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DOI: https://doi.org/10.1007/11527886_63
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