Un estudio de los métodos de reducción del frente de Pareto a una única solución aplicado al problema de resumen extractivo multi-documento

  1. Jesús M. Sánchez-Gómez
  2. Miguel Ángel Vega Rodríguez
  3. Carlos J. Pérez González
Procesamiento del lenguaje natural

ISSN: 1135-5948

Year of publication: 2020

Issue: 65

Pages: 21-28

Type: Article

Export: RIS


SCImago Journal Rank

  • Year 2020
  • SJR Journal Impact: 0.149
  • Best Quartile: Q2
  • Area: Language and Linguistics Quartile: Q2 Rank in area: 452/911
  • Area: Linguistics and Language Quartile: Q3 Rank in area: 505/977
  • Area: Computer Science Applications Quartile: Q4 Rank in area: 1334/2196

Índice Dialnet de Revistas

  • Year 2020
  • Journal Impact: 0.386
  • Field: FILOLOGÍAS Quartile: C1 Rank in field: 9/325
  • Field: LINGÜÍSTICA Quartile: C1 Rank in field: 6/72


  • Social Sciences: B
  • Human Sciences: A


  • Year 2020
  • CiteScore of the Journal : 1.1
  • Area: Language and Linguistics Percentile: 77
  • Area: Linguistics and Language Percentile: 76
  • Area: Computer Science Applications Percentile: 24

Journal Citation Indicator (JCI)

  • Year 2020
  • Journal Citation Indicator (JCI): 0.18
  • Best Quartile: Q4
  • Area: LINGUISTICS Quartile: Q4 Rank in area: 236/262


Automatic summarization methods are currently needed in many different contexts. The extractive multi-document summarization problem tries to cover the main content of a document collection and to reduce the redundant information. The best way to address this task is through a multi-objective optimization approach. The result of this approach is a set of non-dominated solutions or Pareto set. However, since only one summary is needed, the Pareto front must be reduced to a single solution. For this, several methods have been considered, such as the largest hypervolume, the consensus solution, the shortest distance to the ideal point, and the shortest distance to all points. The methods have been tested using datasets from DUC, and they have been evaluated with ROUGE metrics. The results show that consensus solution achieves the best average values.

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