Author, as appears in the article.: Villa-Monte A; Lanzarini L; Corvi J; Bariviera AF

Department: Gestió d'Empreses

URV's Author/s: Fernández Bariviera, Aurelio

Keywords: Text summarization Sentence scoring Neural networks Feature selection Extractive summaries

Abstract: © 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.

Thematic Areas: 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

licence for use: https://creativecommons.org/licenses/by/3.0/es/

ISSN: 10641246

Author's mail: aurelio.fernandez@urv.cat

Author identifier: 0000-0003-1014-1010

Record's date: 2023-02-19

Papper version: info:eu-repo/semantics/acceptedVersion

Link to the original source: https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs179648

Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/

Papper original source: Journal Of Intelligent & Fuzzy Systems. 38 (5): 5579-5588

APA: Villa-Monte A; Lanzarini L; Corvi J; Bariviera AF (2020). Document summarization using a structural metrics based representation.

Article's DOI: 10.3233/JIFS-179648

Entity: Universitat Rovira i Virgili

Journal publication year: 2020

Publication Type: Proceedings Paper