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dc.contributor.supervisor Chali, Yllias
dc.contributor.author Ghiyafeh Davoodi, Fatemeh
dc.contributor.author University of Lethbridge. Faculty of Arts and Science
dc.date.accessioned 2015-10-02T20:21:39Z
dc.date.available 2015-10-02T20:21:39Z
dc.date.issued 2015
dc.identifier.uri https://hdl.handle.net/10133/3759
dc.description.abstract In this thesis, I design a Maximum Coverage problem with KnaPsack constraint (MCKP) based model for extractive multi-document summarization. The model integrates three measures to detect important sentences including Coverage, rewards sentences in regards to their representative level of the whole document, Relevance, focuses to select sentences that related to the given query, and Compression, rewards concise sentences. To generate a summary, I apply an efficient and scalable greedy algorithm. The algorithm has a near optimal solution when its scoring functions are monotone non-decreasing and submodular. I use DUC 2007 dataset to evaluate our proposed method. Investigating the results using ROUGE package shows improvement over two closely related works. The experimental results illustrates that integrating compression in the MCKP-based model, applying semantic similarity measures to detect Relevance measure and also defining all scoring functions as a monotone submodular function result in having a better performance in generating a summary. en_US
dc.language.iso en_CA en_US
dc.publisher Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science en_US
dc.subject greedy algorithm en_US
dc.subject knapsack en_US
dc.subject maximum coverage en_US
dc.subject multi-document en_US
dc.subject summarization en_US
dc.title Semi-extractive multi-document summarization en_US
dc.type Thesis en_US
dc.publisher.faculty Arts and Science en_US
dc.publisher.department Department of Mathematics and Computer Science en_US
dc.degree.level Masters en_US
dc.proquest.subject 0984 en_US
dc.proquestyes Yes en_US
dc.embargo No en_US


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