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
A system for query-specific document summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
User-model based personalized summarization
Information Processing and Management: an International Journal
Generic Summarization Using Non-negative Semantic Variable
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Automatic Personalized Summarization Using Non-negative Matrix Factorization and Relevance Measure
IWSCA '08 Proceedings of the 2008 IEEE International Workshop on Semantic Computing and Applications
Query based summarization using non-negative matrix factorization
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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
Automatic query-based personalized summarization that uses pseudo relevance feedback with NMF
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
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Recently, the necessity of personalized document summarization reflecting user interest from search results is increased. This paper proposes a personalized document summarization method using non-negative semantic feature (NSF) and non-negative semantic variable (NSV) to extract sentences relevant to a user interesting. The proposed method uses NSV to summarize generic summary so that it can extract sentences covering the major topics of the document with respect to user interesting. Besides, it can improve the quality of personalized summaries because the inherent semantics of the documents are well reflected by using NSF and the sentences most relevant to the given query are extracted efficiently by using NSV. The experimental results demonstrate that the proposed method achieves better performance the other methods.