Title

Automatically building templates for entity summary construction

Publication Type

Journal Article

Publication Date

1-2013

Abstract

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.

Keywords

Summary template, LDA, Pattern mining

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

Information Processing and Management

Volume

49

Issue

1

First Page

330

Last Page

340

ISSN

0306-4573

Identifier

10.1016/j.ipm.2012.03.006

Publisher

Elsevier

Additional URL

http://dx.doi.org/10.1016/j.ipm.2012.03.006