Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
An improved extraction pattern representation model for automatic IE pattern acquisition
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Bayesian query-focused summarization
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Preemptive information extraction using unrestricted relation discovery
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Automatic creation of domain templates
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
On-demand information extraction
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Autonomously semantifying wikipedia
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Modeling online reviews with multi-grain topic models
Proceedings of the 17th international conference on World Wide Web
Exploring content models for multi-document summarization
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Automatically generating Wikipedia articles: a structure-aware approach
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Unsupervised relation extraction by mining Wikipedia texts using information from the web
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
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In this paper, we propose a novel approach to automatic generation of summary templates from given collections of summary articles. We first develop an entity-aspect LDA model to simultaneously cluster both sentences and words into aspects. We then apply frequent subtree pattern mining on the dependency parse trees of the clustered and labeled sentences to discover sentence patterns that well represent the aspects. Finally, we use the generated templates to construct summaries for new entities. Key features of our method include automatic grouping of semantically related sentence patterns and automatic identification of template slots that need to be filled in. Also, we implement a new sentence compression algorithm which use dependency tree instead of parser tree. We apply our method on five Wikipedia entity categories and compare our method with three baseline methods. Both quantitative evaluation based on human judgment and qualitative comparison demonstrate the effectiveness and advantages of our method.