User-Oriented Summaries Using a PSO Based Scoring Optimization Method

Autores
Villa Monte, Augusto; Lanzarini, Laura Cristina; Fernández Bariviera, Aurelio; Olivas Varela, José Ángel
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Automatic text summarization tools have a great impact on many fields, such as medicine, law, and scientific research in general. As information overload increases, automatic summaries allow handling the growing volume of documents, usually by assigning weights to the extracted phrases based on their significance in the expected summary. Obtaining the main contents of any given document in less time than it would take to do that manually is still an issue of interest. In this article, a new method is presented that allows automatically generating extractive summaries from documents by adequately weighting sentence scoring features using Particle Swarm Optimization. The key feature of the proposed method is the identification of those features that are closest to the criterion used by the individual when summarizing. The proposed method combines a binary representation and a continuous one, using an original variation of the technique developed by the authors of this paper. Our paper shows that using user labeled information in the training set helps to find better metrics and weights. The empirical results yield an improved accuracy compared to previous methods used in this field.
Instituto de Investigación en Informática
Materia
Ciencias Informáticas
Document summarization
Extractive approach
Scoring-based representation
Sentence feature weighting
Particle swarm optimization
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/118986

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spelling User-Oriented Summaries Using a PSO Based Scoring Optimization MethodVilla Monte, AugustoLanzarini, Laura CristinaFernández Bariviera, AurelioOlivas Varela, José ÁngelCiencias InformáticasDocument summarizationExtractive approachScoring-based representationSentence feature weightingParticle swarm optimizationAutomatic text summarization tools have a great impact on many fields, such as medicine, law, and scientific research in general. As information overload increases, automatic summaries allow handling the growing volume of documents, usually by assigning weights to the extracted phrases based on their significance in the expected summary. Obtaining the main contents of any given document in less time than it would take to do that manually is still an issue of interest. In this article, a new method is presented that allows automatically generating extractive summaries from documents by adequately weighting sentence scoring features using Particle Swarm Optimization. The key feature of the proposed method is the identification of those features that are closest to the criterion used by the individual when summarizing. The proposed method combines a binary representation and a continuous one, using an original variation of the technique developed by the authors of this paper. Our paper shows that using user labeled information in the training set helps to find better metrics and weights. The empirical results yield an improved accuracy compared to previous methods used in this field.Instituto de Investigación en Informática2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/118986enginfo:eu-repo/semantics/altIdentifier/issn/1099-4300info:eu-repo/semantics/altIdentifier/doi/10.3390/e21060617info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T11:00:10Zoai:sedici.unlp.edu.ar:10915/118986Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:00:11.111SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv User-Oriented Summaries Using a PSO Based Scoring Optimization Method
title User-Oriented Summaries Using a PSO Based Scoring Optimization Method
spellingShingle User-Oriented Summaries Using a PSO Based Scoring Optimization Method
Villa Monte, Augusto
Ciencias Informáticas
Document summarization
Extractive approach
Scoring-based representation
Sentence feature weighting
Particle swarm optimization
title_short User-Oriented Summaries Using a PSO Based Scoring Optimization Method
title_full User-Oriented Summaries Using a PSO Based Scoring Optimization Method
title_fullStr User-Oriented Summaries Using a PSO Based Scoring Optimization Method
title_full_unstemmed User-Oriented Summaries Using a PSO Based Scoring Optimization Method
title_sort User-Oriented Summaries Using a PSO Based Scoring Optimization Method
dc.creator.none.fl_str_mv Villa Monte, Augusto
Lanzarini, Laura Cristina
Fernández Bariviera, Aurelio
Olivas Varela, José Ángel
author Villa Monte, Augusto
author_facet Villa Monte, Augusto
Lanzarini, Laura Cristina
Fernández Bariviera, Aurelio
Olivas Varela, José Ángel
author_role author
author2 Lanzarini, Laura Cristina
Fernández Bariviera, Aurelio
Olivas Varela, José Ángel
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Document summarization
Extractive approach
Scoring-based representation
Sentence feature weighting
Particle swarm optimization
topic Ciencias Informáticas
Document summarization
Extractive approach
Scoring-based representation
Sentence feature weighting
Particle swarm optimization
dc.description.none.fl_txt_mv Automatic text summarization tools have a great impact on many fields, such as medicine, law, and scientific research in general. As information overload increases, automatic summaries allow handling the growing volume of documents, usually by assigning weights to the extracted phrases based on their significance in the expected summary. Obtaining the main contents of any given document in less time than it would take to do that manually is still an issue of interest. In this article, a new method is presented that allows automatically generating extractive summaries from documents by adequately weighting sentence scoring features using Particle Swarm Optimization. The key feature of the proposed method is the identification of those features that are closest to the criterion used by the individual when summarizing. The proposed method combines a binary representation and a continuous one, using an original variation of the technique developed by the authors of this paper. Our paper shows that using user labeled information in the training set helps to find better metrics and weights. The empirical results yield an improved accuracy compared to previous methods used in this field.
Instituto de Investigación en Informática
description Automatic text summarization tools have a great impact on many fields, such as medicine, law, and scientific research in general. As information overload increases, automatic summaries allow handling the growing volume of documents, usually by assigning weights to the extracted phrases based on their significance in the expected summary. Obtaining the main contents of any given document in less time than it would take to do that manually is still an issue of interest. In this article, a new method is presented that allows automatically generating extractive summaries from documents by adequately weighting sentence scoring features using Particle Swarm Optimization. The key feature of the proposed method is the identification of those features that are closest to the criterion used by the individual when summarizing. The proposed method combines a binary representation and a continuous one, using an original variation of the technique developed by the authors of this paper. Our paper shows that using user labeled information in the training set helps to find better metrics and weights. The empirical results yield an improved accuracy compared to previous methods used in this field.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
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url http://sedici.unlp.edu.ar/handle/10915/118986
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1099-4300
info:eu-repo/semantics/altIdentifier/doi/10.3390/e21060617
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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