Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Advantages of query biased summaries in information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Yahoo! as an ontology: using Yahoo! categories to describe documents
Proceedings of the eighth international conference on Information and knowledge management
New Methods in Automatic Extracting
Journal of the ACM (JACM)
Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
User Modeling for Adaptive News Access
User Modeling and User-Adapted Interaction
Using and Evaluating User Directed Summaries to Improve Information Access
ECDL '99 Proceedings of the Third European Conference on Research and Advanced Technology for Digital Libraries
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
User-model based personalized summarization
Information Processing and Management: an International Journal
Evaluation of the Effects of User-Sensitivity on Text Summarization
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Incremental Personalised Summarisation with Novelty Detection
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
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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.