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dc.contributor.authorSánchez Gómez, Jesús Manuel-
dc.contributor.authorVega Rodríguez, Miguel Ángel-
dc.contributor.authorPérez Sánchez, Carlos Javier-
dc.description.abstractNowadays, the automatic text summarization is a highly relevant task in many contexts. In particular, query-focused summarization consists of generating a summary from one or multiple documents according to a query given by the user. Additionally, sentiment analysis and opinion mining analyze the polarity of the opinions contained in texts. These two issues are integrated in an approach to produce an opinionated summary according to the user’s query. Thereby, the query-focused sentiment-oriented extractive multi-document text summarization problem entails the optimization of different criteria, specifically, query relevance, redundancy reduction, and sentiment relevance. An adaptation of the metaheuristic population-based crow search algorithm has been designed, implemented, and tested to solve this multi-objective problem. Experiments have been carried out by using datasets from the Text Analysis Conference (TAC) datasets. Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics and the Pearson correlation coefficient have been used for the performance assessment. The results have reported that the proposed approach outperforms the existing methods in the scientific literature, with a percentage improvement of 75.5% for ROUGE-1 score and 441.3% for ROUGE-2 score. It also has been obtained a Pearson correlation coefficient of +0.841, reporting a strong linear positive correlation between the sentiment scores of the generated summaries and the sentiment scores of the queries of the topics.es_ES
dc.description.sponsorshipWork supported by Ministerio de Ciencia, Innovación y Universidades - Spain and Agencia Estatal de Investigación - Spain (Projects PID2019-107299GB-I00/AEI/10.13039/501100011033 and MTM2017-86875-C3-2-R), Junta de Extremadura - Spain (Projects GR18090 and GR18108), and European Union (European Regional Development Fund). Jesus M. Sanchez-Gomez is supported by Junta de Extremadura, Spain and European Union. (European Social Fund) under the doctoral fellowship PD18057.es_ES
dc.format.extent12 p.es_ES
dc.rightsAttribution 4.0 International*
dc.subjectMulti-objective optimizationes_ES
dc.subjectCrow search algorithmes_ES
dc.subjectSentiment-oriented summarizationes_ES
dc.subjectQuery-focused summarizationes_ES
dc.subjectSentiment analysises_ES
dc.subjectAnálisis de los sentimientoses_ES
dc.subjectOptimización multiobjetivoes_ES
dc.subjectResumen centrado en consultases_ES
dc.subjectResumen orientado al sentimientoes_ES
dc.titleSentiment-oriented query-focused text summarization addressed with a multi-objective optimization approaches_ES
dc.subject.unesco1209.01 Estadística Analíticaes_ES
dc.subject.unesco5701.02 Documentación Automatizadaes_ES
dc.subject.unesco12 Matemáticases_ES
dc.subject.unesco3304.13 Dispositivos de Transmisión de Datoses_ES
europeana.dataProviderUniversidad de Extremadura. Españaes_ES
dc.identifier.bibliographicCitationSánchez Gómez, J.M., Vega Rodríguez, M.A., Pérez Sánchez, C.J. (2021). Sentiment-oriented query-focused text summarization addressed with a multi-objective optimization approach. Applied Soft Computing, 113(A), 107915.
dc.contributor.affiliationUniversidad de Extremadura. Departamento de Matemáticases_ES
dc.contributor.affiliationUniversidad de Extremadura. Departamento de Tecnología de los Computadores y de las Comunicacioneses_ES
dc.identifier.publicationtitleApplied Soft Computinges_ES
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