A summary is a comprehensive description that grasps the essence of a subject. A text, a collection of text documents, a query answer can be summarized by simple means such as an automatically generated list of the most frequent words or ”advanced” by a meaningful textual description of the subject. In between these two extremes are summaries by means of selected concepts exploiting background knowledge providing selected key concepts. We address in this paper an approach where conceptual summaries are provided through a conceptualization as given by an ontology. The idea is to restrict a background ontology to the set of concepts that appears in the text to be summarized and therebyl provide a structure, a so-called instantiated ontology, that is specific to the domain of the text and can be used to condense to a summary not only quantitatively but also conceptually covers the subject of the text. In this chapter we introduce different approaches to summarization. We consider a strictly ontologly based approach where summaries are derived solely from the instantiated ontology, a conceptual clustering over the instantiated concepts based on a semantic similarity measure, and an approach based on probabilities.
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- On Deriving Data Summarization through Ontologies to Meet User Preferences
- Springer Berlin Heidelberg
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