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dc.contributor.authorViswanath, Meghana
dc.date.accessioned2014-03-04T18:25:48Z
dc.date.available2014-03-04T18:25:48Z
dc.date.issued2009-12
dc.identifier.otherviswanath_meghana_200912_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/viswanath_meghana_200912_ms
dc.identifier.urihttp://hdl.handle.net/10724/26178
dc.description.abstractThis thesis presents an ontology-based approach to automatic extractive summarization of text. Most of the extractive summarization systems so far have used statistical importance measures to determine importance of sentences. We use a knowledge-based approach which makes use of ontological knowledge to determine sentence importance. The Wikipedia ontology is the source of this knowledge. A sub-graph of the ontology is extracted after mapping the input document onto the ontology. The sub-graph, called the Thematic Graph, contains ontology concepts which match the terms in the document and edges from the ontology which represent relationships between the concepts. Hence, the thematic graph represents the theme of the input document. The thematic graph thus obtained is then analyzed using various graph-based importance measures to determine the relative importance of nodes. These values are used ultimately to decide which sentences are included in the summary for the document.0
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectSemantic Web
dc.subjectExtractive Summarization
dc.subjectOntology
dc.titleOntology-based automatic text summarization
dc.typeThesis
dc.description.degreeMS
dc.description.departmentComputer Science
dc.description.majorComputer Science
dc.description.advisorKrzysztof J. Kochut
dc.description.committeeKrzysztof J. Kochut
dc.description.committeeJohn A. Miller
dc.description.committeeI. Budak Arpinar


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