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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/118986
Ver los metadatos del registro completo
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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 http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/118986 |
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dc.language.none.fl_str_mv |
eng |
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eng |
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info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) |
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openAccess |
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