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An Ontology-Based Approach to Text Summarization

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An Ontology-Based Approach to Text Summarization
Extractive text summarization aims to create a condensed version of one or more source documents by selecting the most informative sentences. Research in text summarization has therefore often focused on measures of the usefulness of sentences for a summary. We present an approach to sentence extraction that maps sentences to nodes of a hierarchical ontology. By considering ontology attributes we are able to improve the semantic representation of a sentence’s information content. The classifier that maps sentences to the taxonomy is trained using search engines and is therefore very flexible and not bound to a specific domain. In our experiments, we train an SVM classifier to identify summary sentences using ontology-based sentence features. Our experimental results show that the ontology-based extraction of sentences outperforms baseline classifiers, leading to higher Rouge scores of summary extracts.
Leonhard Hennig, Winfried Umbrath, Robert Wetzker
Added 29 May 2010
Updated 29 May 2010
Type Conference
Year 2008
Where IAT
Authors Leonhard Hennig, Winfried Umbrath, Robert Wetzker
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