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

• Autores: Jesús M. Sánchez-Gómez, Miguel Ángel Vega Rodríguez , Carlos J. Pérez González
• Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 65, 2020, págs. 21-28
• Idioma: español
• Títulos paralelos:
  • A study of methods for reducing the Pareto front to a single solution applied to the extractive multi-document summarization problem
• Enlaces
  • Texto completo
• Resumen
  • español

    Los métodos de resumen automático son actualmente necesarios en muchos contextos diferentes. El problema de resumen extractivo multi-documento intenta cubrir el contenido principal de una colección de documentos y reducir la información redundante. La mejor manera de abordar esta tarea es mediante un enfoque de optimización multi-objetivo. El resultado de este enfoque es un conjunto de soluciones no dominadas o conjunto de Pareto. Sin embargo, dado que solo se necesita un resumen, se debe reducir el frente de Pareto a una única solución. Para ello, se han considerado varios métodos, como el mayor hipervolumen, la solución consenso, la distancia más corta al punto ideal y la distancia más corta a todos los puntos. Los métodos han sido probados utilizando conjuntos de datos de DUC, y han sido evaluados con las métricas ROUGE. Los resultados revelan que la solución consenso obtiene los mejores valores promedio.

  • English

    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.

• Referencias bibliográficas
  • Aguirre, O. y H. Taboada. 2011. A Clustering Method Based on Dynamic Self Organizing Trees for Post-Pareto Optimality Analysis. Procedia Computer...
  • Alguliev, R. M., R. M. Aliguliyev, y C. A. Mehdiyev. 2011. Sentence selection for generic document summarization using an adaptive differential...
  • 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...
  • Beume, N., C. M. Fonseca, M. López-Ibáñez, L. Paquete, y J. Vahrenhold. 2009. On the Complexity of Computing the Hypervolume Indicator. IEEE...
  • Ferreira, J. C., C. M. Fonseca, y A. GasparCunha. 2007. Methodology to Select Solutions from the Pareto-Optimal Set: A Comparative Study....
  • 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....
  • 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...
  • 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...
  • 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...
  • Sanchez-Gomez, J. M., M. A. VegaRodr´ıguez, y C. J. P´erez. 2018. Extractive multi-document text summarization using a multi-objective artificial...
  • 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....
  • Sudeng, S. y N. Wattanapongsakorn. 2015. Post Pareto-optimal pruning algorithm for multiple objective optimization using specific extended...
  • Taboada, H. A. y D. W. Coit. 2007. Data Clustering of Solutions for Multiple Objective System Reliability Optimization Problems. Qual. Technol....
  • Veerappa, V. y E. Letier. 2011. Understanding Clusters of Optimal Solutions in Multi-Objective Decision Problems. En 19th Requirements Engineering...
  • Wan, X. 2008. An exploration of document impact on graph-based multi-document summarization. En Proceedings of the Conference on Empirical...
  • 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....
  • Zajic, D. M., B. J. Dorr, y J. Lin. 2008. Single-document and multidocument summarization techniques for email threads using sentence compression....
  • Zhao, L., Z. Lu, W. Yun, y W. Wang. 2017. Validation metric based on Mahalanobis distance for models with multiple correlated responses. Reliab....