Autor segons l'article: Villa-Monte A; Lanzarini L; Corvi J; Bariviera AF
Departament: Gestió d'Empreses
Autor/s de la URV: Fernández Bariviera, Aurelio
Paraules clau: Text summarization Sentence scoring Neural networks Feature selection Extractive summaries
Resum: © 2020 - IOS Press and the authors. All rights reserved. Currently, each person produces 1.7MB of information every second in different formats. However, the vast majority of information is text. This has increased the interest to study techniques to automate the identification of the relevant portions of text documents in order to offer as a result an automatic summary. This article presents a technique to extract the most representative sentences of a document taking into account by the user's criteria. These criteria are learned using a neural network, from a minimum set of documents whose sentences have been rated by the user in terms of importance. To verify the performance of the proposed methodology, we used 220 scientific articles from the PLOS Medicine journal published between 2004 and 2016. The results obtained have been very satisfactory.
Àrees temàtiques: Statistics and probability Interdisciplinar General engineering Ensino Engineering (miscellaneous) Engineering (all) Engenharias iv Engenharias iii Economia Computer science, artificial intelligence Ciências ambientais Ciência da computação Biotecnología Artificial intelligence Administração pública e de empresas, ciências contábeis e turismo
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 10641246
Adreça de correu electrònic de l'autor: aurelio.fernandez@urv.cat
Identificador de l'autor: 0000-0003-1014-1010
Data d'alta del registre: 2024-04-27
Versió de l'article dipositat: info:eu-repo/semantics/acceptedVersion
Enllaç font original: https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs179648
Referència a l'article segons font original: Journal Of Intelligent & Fuzzy Systems. 38 (5): 5579-5588
Referència de l'ítem segons les normes APA: Villa-Monte A; Lanzarini L; Corvi J; Bariviera AF (2020). Document summarization using a structural metrics based representation. Journal Of Intelligent & Fuzzy Systems, 38(5), 5579-5588. DOI: 10.3233/JIFS-179648
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
DOI de l'article: 10.3233/JIFS-179648
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2020
Tipus de publicació: Journal Publications
Repositori URV