Constructing literature abstracts by computer: techniques and prospects
Information Processing and Management: an International Journal - Special issue on natural language processing and information retrieval
The identification of important concepts in highly structured technical papers
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Passage-level evidence in document retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic condensation of electronic publications by sentence selection
Information Processing and Management: an International Journal - Special issue: summarizing text
Accurate user directed summarization from existing tools
Proceedings of the seventh international conference on Information and knowledge management
Advantages of query biased summaries in information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning of generic and user-focused summarization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
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)
A new approach to unsupervised text summarization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating Natural Language Processing Systems: An Analysis and Review
Evaluating Natural Language Processing Systems: An Analysis and Review
The use of unlabeled data to improve supervised learning for text summarization
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
User Modeling for Adaptive News Access
User Modeling and User-Adapted Interaction
SUMMAC: a text summarization evaluation
Natural Language Engineering
Producing more readable extracts by revising them
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Automatic evaluation of summaries using N-gram co-occurrence statistics
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLPWorkshop on Automatic summarization - Volume 4
A comparison of rankings produced by summarization evaluation measures
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLPWorkshop on Automatic summarization - Volume 4
Web Intelligence and Agent Systems
The automatic creation of literature abstracts
IBM Journal of Research and Development
Evaluation of a system for personalized summarization of web contents
UM'05 Proceedings of the 10th international conference on User Modeling
User-centred versus system-centred evaluation of a personalization system
Information Processing and Management: an International Journal
GA, MR, FFNN, PNN and GMM based models for automatic text summarization
Computer Speech and Language
Aspect-Based Personalized Text Summarization
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
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
Personalized Summarization Agent Using Non-negative Matrix Factorization
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
An Extractive Text Summarizer Based on Significant Words
ICCPOL '09 Proceedings of the 22nd International Conference on Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy
Automatic personalized text summarization agent using generic relevance weight based on NMF
ICOIN'09 Proceedings of the 23rd international conference on Information Networking
Computational Linguistics
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Automatic query-based personalized summarization that uses pseudo relevance feedback with NMF
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
Text summarisation in progress: a literature review
Artificial Intelligence Review
A pilot study on using profile-based summarisation for interactive search assistance
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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The potential of summary personalization is high, because a summary that would be useless to decide the relevance of a document if summarized in a generic manner, may be useful if the right sentences are selected that match the user interest. In this paper we defend the use of a personalized summarization facility to maximize the density of relevance of selections sent by a personalized information system to a given user. The personalization is applied to the digital newspaper domain and it used a user-model that stores long and short term interests using four reference systems: sections, categories, keywords and feedback terms. 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 results obtained in two personalization systems show that personalized summaries perform better than generic and generic-personalized summaries in terms of identifying documents that satisfy user preferences. We also considered a user-centred direct evaluation that showed a high level of user satisfaction with the summaries.