Generating Templates of Entity Summaries with an Entity-Aspect Model and Pattern Mining
Conference Proceeding Article
In this paper, we propose a novel approach to automatic generation of summary templates from given collections of summary articles. This kind of summary templates can be useful in various applications. 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. 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. We apply our method on five Wikipedia entity categories and compare our method with two baseline methods. Both quantitative evaluation based on human judgment and qualitative comparison demonstrate the effectiveness and advantages of our method.
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
City or Country
LI, Peng; JIANG, Jing; and WANG, Yinglin.
Generating Templates of Entity Summaries with an Entity-Aspect Model and Pattern Mining. (2010). Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. 640-649. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/641