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Automatically building templates for entity summary construction
Abstract In this paper, we propose a novel approachIn 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. © 2012 Elsevier Ltd. All rights reserved. © 2012 Elsevier Ltd. All rights reserved.
Abstractsub In this paper, we propose a novel approachIn 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. © 2012 Elsevier Ltd. All rights reserved. © 2012 Elsevier Ltd. All rights reserved.
Bibtextype article  +
Doi 10.1016/j.ipm.2012.03.006  +
Has author Li P. + , Yafang Wang + , Jian Jiang +
Has extra keyword Automatic Generation + , Automatic identification + , Baseline methods + , Dependency trees + , Frequent subtrees + , Human judgments + , Key feature + , LDA + , Parse trees + , Pattern mining + , Quantitative evaluation + , Sentence compression + , Summary template + , Wikipedia + , Automation + , Forestry + , Linguistics + , Trees (mathematics) + , Abstracts + , Language + , Pattern Recognition +
Has keyword LDA + , Pattern mining + , Summary template +
Issn 3064573  +
Issue 1  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 330–340  +
Published in Information Processing and Management +
Title Automatically building templates for entity summary construction +
Type journal article  +
Volume 49  +
Year 2013 +
Creation dateThis property is a special property in this wiki. 7 November 2014 09:30:04  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Journal articles  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 7 November 2014 09:30:04  +
DateThis property is a special property in this wiki. 2013  +
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