{"id":{"repo_id":"lethbridge","oai_identifier":"oai:opus.uleth.ca:10133/4841"},"canonical_url":"https://search.dev.ndltd.org/etd/lethbridge/oai:opus.uleth.ca:10133/4841","repository":{"repo_id":"lethbridge","name":"University of Lethbridge","base_url":"https://opus.uleth.ca/server/oai/request"},"display":{"title":"Toward abstractive multi-document summarization using submodular function-based framework, sentence compression and merging","abstract":"Automatic multi-document summarization is a process of generating a summary that contains the most important information from multiple documents. In this thesis, we design an automatic multi-document summarization system using different abstraction-based methods and submodularity. Our proposed model considers summarization as a budgeted submodular function maximization problem. The model integrates three important measures of a summary - namely importance, coverage, and non-redundancy, and we design a submodular function for each of them. In addition, we integrate sentence compression and sentence merging. When evaluated on the DUC 2004 data set, our generic summarizer has outperformed the state-of-the-art summarization systems in terms of ROUGE-1 recall and f1-measure. For query-focused summarization, we used the DUC 2007 data set where our system achieves statistically similar results to several well-established methods in terms of the ROUGE-2 measure.","abstract_html":"Automatic multi-document summarization is a process of generating a summary that contains the most important information from multiple documents. In this thesis, we design an automatic multi-document summarization system using different abstraction-based methods and submodularity. Our proposed model considers summarization as a budgeted submodular function maximization problem. The model integrates three important measures of a summary - namely importance, coverage, and non-redundancy, and we design a submodular function for each of them. In addition, we integrate sentence compression and sentence merging. When evaluated on the DUC 2004 data set, our generic summarizer has outperformed the state-of-the-art summarization systems in terms of ROUGE-1 recall and f1-measure. For query-focused summarization, we used the DUC 2007 data set where our system achieves statistically similar results to several well-established methods in terms of the ROUGE-2 measure.","abstract_has_math":false,"creators":["Tanvee, Moin Mahmud","University of Lethbridge. Faculty of Arts and Science"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016","date_published":"2016","updated_at":"2026-07-27T20:02:38Z","subjects":["automatic text summarization","abstraction-based","submodular function","generic-focused summarization","query-focused summarization","greedy algorithm","Natural language processing (Computer science) -- Research","Querying (Computer science)","Database searching","Parsing (Computer grammar)","Information retrieval","Question-answering systems -- Research","Computer science -- Mathematics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:10133/4841"],"render_values":[{"text":"hdl:10133/4841","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2016"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["automatic text summarization","abstraction-based","submodular function","generic-focused summarization","query-focused summarization","greedy algorithm","Natural language processing (Computer science) -- Research","Querying (Computer science)","Database searching","Parsing (Computer grammar)","Information retrieval","Question-answering systems -- Research","Computer science -- Mathematics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:10133/4841"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.other","label":"Dc Description Other","values":["Automatic multi-document summarization is a process of generating a summary that contains the most important information from multiple documents. In this thesis, we design an automatic multi-document summarization system using different abstraction-based methods and submodularity. Our proposed model considers summarization as a budgeted submodular function maximization problem. The model integrates three important measures of a summary - namely importance, coverage, and non-redundancy, and we design a submodular function for each of them. In addition, we integrate sentence compression and sentence merging. When evaluated on the DUC 2004 data set, our generic summarizer has outperformed the state-of-the-art summarization systems in terms of ROUGE-1 recall and f1-measure. For query-focused summarization, we used the DUC 2007 data set where our system achieves statistically similar results to several well-established methods in terms of the ROUGE-2 measure."]},{"key":"dc:title","label":"Title","values":["Toward abstractive multi-document summarization using submodular function-based framework, sentence compression and merging"]}]}],"canonical_facts":{"dc:date.issued":["2016"],"dc:description.other":["Automatic multi-document summarization is a process of generating a summary that contains the most important information from multiple documents. In this thesis, we design an automatic multi-document summarization system using different abstraction-based methods and submodularity. Our proposed model considers summarization as a budgeted submodular function maximization problem. The model integrates three important measures of a summary - namely importance, coverage, and non-redundancy, and we design a submodular function for each of them. In addition, we integrate sentence compression and sentence merging. When evaluated on the DUC 2004 data set, our generic summarizer has outperformed the state-of-the-art summarization systems in terms of ROUGE-1 recall and f1-measure. For query-focused summarization, we used the DUC 2007 data set where our system achieves statistically similar results to several well-established methods in terms of the ROUGE-2 measure."],"dc:identifier":["hdl:10133/4841"],"dc:subject":["automatic text summarization","abstraction-based","submodular function","generic-focused summarization","query-focused summarization","greedy algorithm","Natural language processing (Computer science) -- Research","Querying (Computer science)","Database searching","Parsing (Computer grammar)","Information retrieval","Question-answering systems -- Research","Computer science -- Mathematics"],"dc:title":["Toward abstractive multi-document summarization using submodular function-based framework, sentence compression and merging"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T20:02:38Z"}