Event-based summarization using a centrality-as-relevance model
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/13278 |
Resumo: | Event detection is a fundamental information extraction task, which has been explored largely in the context of question answering, topic detection and tracking, knowledge base population, news recommendation, and automatic summarization. In this article, we explore an event detection framework to improve a key phrase-guided centrality-based summarization model. Event detection is based on the fuzzy fingerprint method, which is able to detect all types of events in the ACE 2005 Multilingual Corpus. Our base summarization approach is a two-stage method that starts by extracting a collection of key phrases that will be used to help the centrality-as-relevance retrieval model. We explored three different ways to integrate event information, achieving state-of-the-art results in text and speech corpora: (1) filtering of nonevents, (2) event fingerprints as features, and (3) combination of filtering of nonevents and event fingerprints as features. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Event-based summarization using a centrality-as-relevance modelEvent detectionExtractive summarizationPassage retrievalAutomatic key phrase extractionCentralityEvent detection is a fundamental information extraction task, which has been explored largely in the context of question answering, topic detection and tracking, knowledge base population, news recommendation, and automatic summarization. In this article, we explore an event detection framework to improve a key phrase-guided centrality-based summarization model. Event detection is based on the fuzzy fingerprint method, which is able to detect all types of events in the ACE 2005 Multilingual Corpus. Our base summarization approach is a two-stage method that starts by extracting a collection of key phrases that will be used to help the centrality-as-relevance retrieval model. We explored three different ways to integrate event information, achieving state-of-the-art results in text and speech corpora: (1) filtering of nonevents, (2) event fingerprints as features, and (3) combination of filtering of nonevents and event fingerprints as features.Springer2017-05-10T10:10:15Z2017-01-01T00:00:00Z20172019-03-22T11:18:32Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/13278eng0219-137710.1007/s10115-016-0966-4Marujo, L.Ribeiro, R.Gershman, A.de Matos, D. M.Neto, J. P.Carbonell, J.info:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-09T17:47:42Zoai:repositorio.iscte-iul.pt:10071/13278Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:23:10.631471Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Event-based summarization using a centrality-as-relevance model |
title |
Event-based summarization using a centrality-as-relevance model |
spellingShingle |
Event-based summarization using a centrality-as-relevance model Marujo, L. Event detection Extractive summarization Passage retrieval Automatic key phrase extraction Centrality |
title_short |
Event-based summarization using a centrality-as-relevance model |
title_full |
Event-based summarization using a centrality-as-relevance model |
title_fullStr |
Event-based summarization using a centrality-as-relevance model |
title_full_unstemmed |
Event-based summarization using a centrality-as-relevance model |
title_sort |
Event-based summarization using a centrality-as-relevance model |
author |
Marujo, L. |
author_facet |
Marujo, L. Ribeiro, R. Gershman, A. de Matos, D. M. Neto, J. P. Carbonell, J. |
author_role |
author |
author2 |
Ribeiro, R. Gershman, A. de Matos, D. M. Neto, J. P. Carbonell, J. |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Marujo, L. Ribeiro, R. Gershman, A. de Matos, D. M. Neto, J. P. Carbonell, J. |
dc.subject.por.fl_str_mv |
Event detection Extractive summarization Passage retrieval Automatic key phrase extraction Centrality |
topic |
Event detection Extractive summarization Passage retrieval Automatic key phrase extraction Centrality |
description |
Event detection is a fundamental information extraction task, which has been explored largely in the context of question answering, topic detection and tracking, knowledge base population, news recommendation, and automatic summarization. In this article, we explore an event detection framework to improve a key phrase-guided centrality-based summarization model. Event detection is based on the fuzzy fingerprint method, which is able to detect all types of events in the ACE 2005 Multilingual Corpus. Our base summarization approach is a two-stage method that starts by extracting a collection of key phrases that will be used to help the centrality-as-relevance retrieval model. We explored three different ways to integrate event information, achieving state-of-the-art results in text and speech corpora: (1) filtering of nonevents, (2) event fingerprints as features, and (3) combination of filtering of nonevents and event fingerprints as features. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-05-10T10:10:15Z 2017-01-01T00:00:00Z 2017 2019-03-22T11:18:32Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10071/13278 |
url |
http://hdl.handle.net/10071/13278 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0219-1377 10.1007/s10115-016-0966-4 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/embargoedAccess |
eu_rights_str_mv |
embargoedAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799134793630220288 |