Automatically building templates for entity summary construction
|Automatically building templates for entity summary construction|
|Author(s)||Li P., Wang Y., Jiang J.|
|Published in||Information Processing and Management|
|Keyword(s)||LDA, Pattern mining, Summary template (Extra: 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, Forestry, Languages, Pattern Recognition)|
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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. © 2012 Elsevier Ltd. All rights reserved.
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