{"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. 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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:contributor.supervisor":["Chali, Yllias"],"dc:creator":["Aryal, Chudamani","University of Lethbridge. 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