Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2010

Abstract

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.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

48th Annual Meeting of the Association for Computational Linguistics 2010: Uppsala, Sweden, July 11-16: Proceedings

First Page

640

Last Page

649

ISBN

9781617388088

Publisher

ACL

City or Country

Stroudsburg, PA

Copyright Owner and License

Publisher

Additional URL

https://www.aclweb.org/anthology/P/P10/P10-1066.pdf

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