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IMPROVED DOCUMENT SUMMARIZATION AND TAG CLOUDS VIA SINGULAR VALUE DECOMPOSITION

Automated summarization is a difficult task. World-class summarizers can provide only "best guesses" of which sentences encapsulate the important content from within a set of documents. As automated systems continue to improve, users are still not given the means to observe complex relationships between seemingly independent concepts. In this research we used singular value decompositions to organize concepts and determine the best candidate sentences for an automated summary. The results from this straightforward attempt were comparable to world-class summarizers. We then included a clustered tag cloud, using a singular value decomposition to measure term "interestingness" with respect to the set of documents. The combination of best candidate sentences and tag clouds provided a more inclusive summary than a traditionally-developed summarizer alone. / Thesis (Master, Computing) -- Queen's University, 2008-09-24 16:31:25.261

Identiferoai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OKQ.1974/1476
Date25 September 2008
CreatorsProvost, JAMES
ContributorsQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))
Source SetsLibrary and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada
LanguageEnglish, English
Detected LanguageEnglish
TypeThesis
Format949859 bytes, application/pdf
RightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
RelationCanadian theses

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