Evaluation of Causal Sentences in Automated Summaries

Autores
Puente Águeda, Cristina; Villa Monte, Augusto; Lanzarini, Laura Cristina; Sobrino Cerdeiriña, Alejandro; Olivas Varela, José Ángel
Año de publicación
2017
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión enviada
Descripción
This paper presents an experiment to show the importance of causal sentences in summaries. Presumably, causal sentences hold relevant information and thus summaries should contain them. We perform an experiment to refute or validate this hypothesis. We have selected 28 medical documents to extract and analyze causal and conditional sentences from medical texts. Once retrieved, classic metrics are used to determine the relevance of the causal content among all the sentences in the document and, so, to evaluate if they are important enough to make a better summary. Finally, a comparison table to explore the results is showed and some conclusions are outlined.
Materia
Ingenierías y Tecnologías
causality
causal sentences
automatic summaries
sentence scoring metrics
Soft Computing
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
CIC Digital (CICBA)
Institución
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
OAI Identificador
oai:digital.cic.gba.gob.ar:11746/8593
Acceder
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spelling Evaluation of Causal Sentences in Automated SummariesPuente Águeda, CristinaVilla Monte, AugustoLanzarini, Laura CristinaSobrino Cerdeiriña, AlejandroOlivas Varela, José ÁngelIngenierías y Tecnologíascausalitycausal sentencesautomatic summariessentence scoring metricsSoft ComputingThis paper presents an experiment to show the importance of causal sentences in summaries. Presumably, causal sentences hold relevant information and thus summaries should contain them. We perform an experiment to refute or validate this hypothesis. We have selected 28 medical documents to extract and analyze causal and conditional sentences from medical texts. Once retrieved, classic metrics are used to determine the relevance of the causal content among all the sentences in the document and, so, to evaluate if they are important enough to make a better summary. Finally, a comparison table to explore the results is showed and some conclusions are outlined.2017info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/submittedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/8593enginfo:eu-repo/semantics/altIdentifier/hdl/11531/22683info:eu-repo/semantics/altIdentifier/doi/10.1109/FUZZ-IEEE.2017.8015666info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/reponame:CIC Digital (CICBA)instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Airesinstacron:CICBA2025-10-23T11:14:43Zoai:digital.cic.gba.gob.ar:11746/8593Institucionalhttp://digital.cic.gba.gob.arOrganismo científico-tecnológicoNo correspondehttp://digital.cic.gba.gob.ar/oai/snrdmarisa.degiusti@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:94412025-10-23 11:14:44.095CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse
dc.title.none.fl_str_mv Evaluation of Causal Sentences in Automated Summaries
title Evaluation of Causal Sentences in Automated Summaries
spellingShingle Evaluation of Causal Sentences in Automated Summaries
Puente Águeda, Cristina
Ingenierías y Tecnologías
causality
causal sentences
automatic summaries
sentence scoring metrics
Soft Computing
title_short Evaluation of Causal Sentences in Automated Summaries
title_full Evaluation of Causal Sentences in Automated Summaries
title_fullStr Evaluation of Causal Sentences in Automated Summaries
title_full_unstemmed Evaluation of Causal Sentences in Automated Summaries
title_sort Evaluation of Causal Sentences in Automated Summaries
dc.creator.none.fl_str_mv Puente Águeda, Cristina
Villa Monte, Augusto
Lanzarini, Laura Cristina
Sobrino Cerdeiriña, Alejandro
Olivas Varela, José Ángel
author Puente Águeda, Cristina
author_facet Puente Águeda, Cristina
Villa Monte, Augusto
Lanzarini, Laura Cristina
Sobrino Cerdeiriña, Alejandro
Olivas Varela, José Ángel
author_role author
author2 Villa Monte, Augusto
Lanzarini, Laura Cristina
Sobrino Cerdeiriña, Alejandro
Olivas Varela, José Ángel
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ingenierías y Tecnologías
causality
causal sentences
automatic summaries
sentence scoring metrics
Soft Computing
topic Ingenierías y Tecnologías
causality
causal sentences
automatic summaries
sentence scoring metrics
Soft Computing
dc.description.none.fl_txt_mv This paper presents an experiment to show the importance of causal sentences in summaries. Presumably, causal sentences hold relevant information and thus summaries should contain them. We perform an experiment to refute or validate this hypothesis. We have selected 28 medical documents to extract and analyze causal and conditional sentences from medical texts. Once retrieved, classic metrics are used to determine the relevance of the causal content among all the sentences in the document and, so, to evaluate if they are important enough to make a better summary. Finally, a comparison table to explore the results is showed and some conclusions are outlined.
description This paper presents an experiment to show the importance of causal sentences in summaries. Presumably, causal sentences hold relevant information and thus summaries should contain them. We perform an experiment to refute or validate this hypothesis. We have selected 28 medical documents to extract and analyze causal and conditional sentences from medical texts. Once retrieved, classic metrics are used to determine the relevance of the causal content among all the sentences in the document and, so, to evaluate if they are important enough to make a better summary. Finally, a comparison table to explore the results is showed and some conclusions are outlined.
publishDate 2017
dc.date.none.fl_str_mv 2017
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dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/hdl/11531/22683
info:eu-repo/semantics/altIdentifier/doi/10.1109/FUZZ-IEEE.2017.8015666
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
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