{"id":{"repo_id":"lethbridge","oai_identifier":"oai:opus.uleth.ca:10133/5400"},"canonical_url":"https://search.dev.ndltd.org/etd/lethbridge/oai:opus.uleth.ca:10133/5400","repository":{"repo_id":"lethbridge","name":"University of Lethbridge","base_url":"https://opus.uleth.ca/server/oai/request"},"display":{"title":"Query-Focused Abstractive Summarization using Neural Networks","abstract":"Query-focused abstractive document summarization (QFADS) is a process of shortening a document into a summary while keeping the context of query in mind. We implemented a model consisting of a novel selective mechanism for QFADS. A selective mechanism was used for improving the representation of a long input (passage) sequence. We conducted experiments on the Debatepedia dataset, a recently developed dataset for query-focused abstractive summarization task, which showed that our model outperforms the state-of-the-art model in all ROUGE scores. Also, we proposed three models all of which consist of a coarse-to-fine approach and a novel selective mechanism for query-focused abstractive multi document summarization (QFAMDS). The coarse-to-fine approach was used to reduce the length of the passage input from multiple documents. We conducted experiments on the MS MARCO dataset, a recently developed large scale dataset by Microsoft for reading comprehension, and have reported our scores using various evaluation metrics.","abstract_html":"Query-focused abstractive document summarization (QFADS) is a process of shortening a document into a summary while keeping the context of query in mind. We implemented a model consisting of a novel selective mechanism for QFADS. A selective mechanism was used for improving the representation of a long input (passage) sequence. We conducted experiments on the Debatepedia dataset, a recently developed dataset for query-focused abstractive summarization task, which showed that our model outperforms the state-of-the-art model in all ROUGE scores. Also, we proposed three models all of which consist of a coarse-to-fine approach and a novel selective mechanism for query-focused abstractive multi document summarization (QFAMDS). The coarse-to-fine approach was used to reduce the length of the passage input from multiple documents. We conducted experiments on the MS MARCO dataset, a recently developed large scale dataset by Microsoft for reading comprehension, and have reported our scores using various evaluation metrics.","abstract_has_math":false,"creators":["Aryal, Chudamani","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":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-27T20:02:08Z","subjects":["Neural networks","Query-focused abstractive document summarization","Query-focused abstractive multi document summarization"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:10133/5400"],"render_values":[{"text":"hdl:10133/5400","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":["2019"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Neural networks","Query-focused abstractive document summarization","Query-focused abstractive multi document summarization"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:10133/5400"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.other","label":"Dc Description Other","values":["Query-focused abstractive document summarization (QFADS) is a process of shortening a document into a summary while keeping the context of query in mind. We implemented a model consisting of a novel selective mechanism for QFADS. A selective mechanism was used for improving the representation of a long input (passage) sequence. We conducted experiments on the Debatepedia dataset, a recently developed dataset for query-focused abstractive summarization task, which showed that our model outperforms the state-of-the-art model in all ROUGE scores. Also, we proposed three models all of which consist of a coarse-to-fine approach and a novel selective mechanism for query-focused abstractive multi document summarization (QFAMDS). The coarse-to-fine approach was used to reduce the length of the passage input from multiple documents. We conducted experiments on the MS MARCO dataset, a recently developed large scale dataset by Microsoft for reading comprehension, and have reported our scores using various evaluation metrics."]},{"key":"dc:title","label":"Title","values":["Query-Focused Abstractive Summarization using Neural Networks"]}]}],"canonical_facts":{"dc:date.issued":["2019"],"dc:description.other":["Query-focused abstractive document summarization (QFADS) is a process of shortening a document into a summary while keeping the context of query in mind. We implemented a model consisting of a novel selective mechanism for QFADS. A selective mechanism was used for improving the representation of a long input (passage) sequence. We conducted experiments on the Debatepedia dataset, a recently developed dataset for query-focused abstractive summarization task, which showed that our model outperforms the state-of-the-art model in all ROUGE scores. Also, we proposed three models all of which consist of a coarse-to-fine approach and a novel selective mechanism for query-focused abstractive multi document summarization (QFAMDS). The coarse-to-fine approach was used to reduce the length of the passage input from multiple documents. We conducted experiments on the MS MARCO dataset, a recently developed large scale dataset by Microsoft for reading comprehension, and have reported our scores using various evaluation metrics."],"dc:identifier":["hdl:10133/5400"],"dc:subject":["Neural networks","Query-focused abstractive document summarization","Query-focused abstractive multi document summarization"],"dc:title":["Query-Focused Abstractive Summarization using Neural Networks"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T20:02:08Z"}