Applying Siamese Hierarchical Attention Neural Networks for multi-document summarization

[EN] In this paper, we present an approach to multi-document summarization based on Siamese Hierarchical Attention Neural Networks. The attention mechanism of Hierarchical Attention Networks, provides a score to each sentence in function of its relevance in the classification process. For the summar...

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Detalhes bibliográficos
Autores: González-Barba, José Ángel, Julien Delonca, Segarra Soriano, Encarnación, Sanchís Arnal, Emilio|||0000-0002-6737-4723, García-Granada, Fernando|||0000-0003-2213-4213
Formato: artículo
Fecha de publicación:2019
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/140216
Acesso em linha:https://riunet.upv.es/handle/10251/140216
Access Level:acceso abierto
Palavra-chave:Siamese hierarchical attention networks
Multi-document summarization
LENGUAJES Y SISTEMAS INFORMATICOS
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spelling Applying Siamese Hierarchical Attention Neural Networks for multi-document summarizationGonzález-Barba, José ÁngelJulien DeloncaSegarra Soriano, EncarnaciónSanchís Arnal, Emilio|||0000-0002-6737-4723García-Granada, Fernando|||0000-0003-2213-4213Siamese hierarchical attention networksMulti-document summarizationLENGUAJES Y SISTEMAS INFORMATICOS[EN] In this paper, we present an approach to multi-document summarization based on Siamese Hierarchical Attention Neural Networks. The attention mechanism of Hierarchical Attention Networks, provides a score to each sentence in function of its relevance in the classification process. For the summarization process, only the scores of sentences are used to rank them and select the most salient sentences. In this work we explore the adaptability of this model to the problem of multi-document summarization (typically very long documents where the straightforward application of neural networks tends to fail). The experiments were carried out using the CNN/DailyMail as training corpus, and the DUC-2007 as test corpus. Despite the difference between training set (CNN/DailyMail) and test set (DUC-2007) characteristics, the results show the adequacy of this approach to multi-document summarization.This work has been partially supported by the Spanish MINECO and FEDER founds under project AMIC (TIN2017-85854-C4-2-R). Work of Jose-Angel Gonzalez is also financed by Universitat Politecnica de Valencia under grant PAID-01-17.Sociedad Española para el Procesamiento del Lenguaje NaturalDepartamento de Sistemas Informáticos y ComputaciónEscuela Técnica Superior de Ingeniería Geodésica, Cartográfica y TopográficaEscuela Técnica Superior de Ingeniería InformáticaInstituto Universitario Valenciano de Investigación en Inteligencia ArtificialAgencia Estatal de InvestigaciónUniversitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20192019-09-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/140216reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengUniversitat Politècnica de València https://doi.org/10.13039/501100004233 PAID-01-17Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 TIN2017-85854-C4-2-R AMIC-UPV: ANALISIS AFECTIVO DE INFORMACION MULTIMEDIA CON COMUNICACION INCLUSIVA Y NATURALopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1402162026-06-13T07:49:27Z
dc.title.none.fl_str_mv Applying Siamese Hierarchical Attention Neural Networks for multi-document summarization
title Applying Siamese Hierarchical Attention Neural Networks for multi-document summarization
spellingShingle Applying Siamese Hierarchical Attention Neural Networks for multi-document summarization
González-Barba, José Ángel
Siamese hierarchical attention networks
Multi-document summarization
LENGUAJES Y SISTEMAS INFORMATICOS
title_short Applying Siamese Hierarchical Attention Neural Networks for multi-document summarization
title_full Applying Siamese Hierarchical Attention Neural Networks for multi-document summarization
title_fullStr Applying Siamese Hierarchical Attention Neural Networks for multi-document summarization
title_full_unstemmed Applying Siamese Hierarchical Attention Neural Networks for multi-document summarization
title_sort Applying Siamese Hierarchical Attention Neural Networks for multi-document summarization
dc.creator.none.fl_str_mv González-Barba, José Ángel
Julien Delonca
Segarra Soriano, Encarnación
Sanchís Arnal, Emilio|||0000-0002-6737-4723
García-Granada, Fernando|||0000-0003-2213-4213
author González-Barba, José Ángel
author_facet González-Barba, José Ángel
Julien Delonca
Segarra Soriano, Encarnación
Sanchís Arnal, Emilio|||0000-0002-6737-4723
García-Granada, Fernando|||0000-0003-2213-4213
author_role author
author2 Julien Delonca
Segarra Soriano, Encarnación
Sanchís Arnal, Emilio|||0000-0002-6737-4723
García-Granada, Fernando|||0000-0003-2213-4213
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Sistemas Informáticos y Computación
Escuela Técnica Superior de Ingeniería Geodésica, Cartográfica y Topográfica
Escuela Técnica Superior de Ingeniería Informática
Instituto Universitario Valenciano de Investigación en Inteligencia Artificial
Agencia Estatal de Investigación
Universitat Politècnica de València
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Siamese hierarchical attention networks
Multi-document summarization
LENGUAJES Y SISTEMAS INFORMATICOS
topic Siamese hierarchical attention networks
Multi-document summarization
LENGUAJES Y SISTEMAS INFORMATICOS
description [EN] In this paper, we present an approach to multi-document summarization based on Siamese Hierarchical Attention Neural Networks. The attention mechanism of Hierarchical Attention Networks, provides a score to each sentence in function of its relevance in the classification process. For the summarization process, only the scores of sentences are used to rank them and select the most salient sentences. In this work we explore the adaptability of this model to the problem of multi-document summarization (typically very long documents where the straightforward application of neural networks tends to fail). The experiments were carried out using the CNN/DailyMail as training corpus, and the DUC-2007 as test corpus. Despite the difference between training set (CNN/DailyMail) and test set (DUC-2007) characteristics, the results show the adequacy of this approach to multi-document summarization.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-09-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/140216
url https://riunet.upv.es/handle/10251/140216
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Universitat Politècnica de València https://doi.org/10.13039/501100004233 PAID-01-17
Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 TIN2017-85854-C4-2-R AMIC-UPV: ANALISIS AFECTIVO DE INFORMACION MULTIMEDIA CON COMUNICACION INCLUSIVA Y NATURAL
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Sociedad Española para el Procesamiento del Lenguaje Natural
publisher.none.fl_str_mv Sociedad Española para el Procesamiento del Lenguaje Natural
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869422520223924224
score 15,301603

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