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

Argitalpen urtea: 2020

Zenbakia: 65

Orrialdeak: 21-28

Mota: Artikulua

Beste argitalpen batzuk: Procesamiento del lenguaje natural


SCImago Journal Rank

  • Urtea 2020
  • Aldizkariaren SJR eragina: 0.149
  • Kuartil nagusia: Q3
  • Arloa: Linguistics and Language Kuartila: Q3 Postua arloan: 540/1150
  • Arloa: Computer Science Applications Kuartila: Q4 Postua arloan: 1336/2240

Índice Dialnet de Revistas

  • Urtea 2020
  • Aldizkariaren eragin faktorea: 0,370
  • Eremua: LINGÜÍSTICA Kuartila: C1 Postua eremuan: 8/72
  • Eremua: FILOLOGÍAS Kuartila: C1 Postua eremuan: 11/327


  • Gizarte Zientziak: B
  • Giza Zientziak: A

Scopus CiteScore

  • Urtea 2020
  • Aldizkariaren CiteScore-a: 1.0
  • Arloa: Language and Linguistics Pertzentila: 72
  • Arloa: Linguistics and Language Pertzentila: 71
  • Arloa: Computer Science Applications Pertzentila: 20

Journal Citation Indicator (JCI)

  • Urtea 2020
  • Aldizkariaren JCI eragina: 0.18
  • Kuartil nagusia: Q4
  • Arloa: LINGUISTICS Kuartila: Q4 Postua arloan: 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.

Erreferentzia bibliografikoak

  • Aguirre, O. y H. Taboada. 2011. A Clustering Method Based on Dynamic Self Organizing Trees for Post-Pareto Optimality Analysis. Procedia Computer Science, 6:195–200.
  • Alguliev, R. M., R. M. Aliguliyev, y C. A. Mehdiyev. 2011. Sentence selection for generic document summarization using an adaptive differential evolution algorithm. Swarm Evol. Comput., 1(4):213–222.
  • Antipova, E., C. Pozo, G. Guill´en-Gosálbez, D. Boer, L. F. Cabeza, y L. Jiménez. 2015. On the use of filters to facilitate the postoptimal analysis of the Pareto solutions in multi-objective optimization. Comput. Chem. Eng., 74:48–58.
  • Beume, N., C. M. Fonseca, M. López-Ibáñez, L. Paquete, y J. Vahrenhold. 2009. On the Complexity of Computing the Hypervolume Indicator. IEEE Trans. Evol. Comput., 13(5):1075–1082.
  • Ferreira, J. C., C. M. Fonseca, y A. GasparCunha. 2007. Methodology to Select Solutions from the Pareto-Optimal Set: A Comparative Study. En Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, p´aginas 789–796. ACM.
  • Hashimi, H., A. Hafez, y H. Mathkour. 2015. Selection criteria for text mining approaches. Comput. Hum. Behav., 51:729–733.
  • Lin, C.-Y. 2004. ROUGE: A package for automatic evaluation of summaries. En Proceedings of the ACL-04 Workshop, volumen 8, p´aginas 74–81. ACL.
  • NIST. 2014. Document Understanding Conferences. http://duc.nist.gov. Ultimo acceso: 11 de agosto de 2020.
  • Padhye, N. y K. Deb. 2011. Multi-objective optimisation and multi-criteria decision making in SLS using evolutionary approaches. Rapid Prototyping Journal, 17(6):458–478.
  • Pérez, C. J., M. A. Vega-Rodríguez, K. Reder, y M. Fl¨orke. 2017. A Multi-Objective Artificial Bee Colony-based optimization approach to design water quality monitoring networks in river basins. Journal of Cleaner Production, 166:579–589.
  • Ristad, E. S. y P. N. Yianilos. 1998. Learning string-edit distance. IEEE Trans. Pattern Anal. Mach. Intell., 20(5):522–532.
  • Saleh, H. H., N. J. Kadhim, y B. A. Attea. 2015. A genetic based optimization model for extractive multi-document text summarization. Iraqi Journal of Science, 56(2):1489–1498.
  • Sanchez-Gomez, J. M., M. A. VegaRodr´ıguez, y C. J. P´erez. 2018. Extractive multi-document text summarization using a multi-objective artificial bee colony optimization approach. Knowledge-Based Syst., 159:1–8.
  • Siwale, I. 2013. Practical Multi-Objective Programming. Informe t´ecnico, Technical Report RD-14-2013. APEX Research.
  • Soylu, B. y S. K. Ulusoy. 2011. A preference ordered classification for a multi-objective max–min redundancy allocation problem. Comput. Oper. Res., 38(12):1855–1866.
  • Sudeng, S. y N. Wattanapongsakorn. 2015. Post Pareto-optimal pruning algorithm for multiple objective optimization using specific extended angle dominance. Eng. Appl. Artif. Intell., 38:221–236.
  • Taboada, H. A. y D. W. Coit. 2007. Data Clustering of Solutions for Multiple Objective System Reliability Optimization Problems. Qual. Technol. Quant. Manag., 4(2):191–210.
  • Veerappa, V. y E. Letier. 2011. Understanding Clusters of Optimal Solutions in Multi-Objective Decision Problems. En 19th Requirements Engineering Conference, páginas 89–98. IEEE.
  • Wan, X. 2008. An exploration of document impact on graph-based multi-document summarization. En Proceedings of the Conference on Empirical Methods in NLP, páginas 755–762. ACL.
  • Wu, L., X. Xu, X. Ye, y X. Zhu. 2015. Repeat and near-repeat burglaries and offender involvement in a large Chinese city. Cartogr. Geogr. Inf. Sci., 42(2):178–189.
  • Zajic, D. M., B. J. Dorr, y J. Lin. 2008. Single-document and multidocument summarization techniques for email threads using sentence compression. Inf. Process. Manage., 4(4):1600–1610.
  • Zhao, L., Z. Lu, W. Yun, y W. Wang. 2017. Validation metric based on Mahalanobis distance for models with multiple correlated responses. Reliab. Eng. Syst. Saf., 159:80–89.