Comparison of automatic methods for reducing the Pareto front to a single solution applied to multi-document text summarization

Abstract

Nowadays, automatic text summarization methods are needed in many different contexts. Extractive multi-document summarization approaches are intended to, simultaneously, synthesize the main content of a document collection and reduce the redundant information. Multi-objective optimization seems to be the natural way to address this problem. The result of applying a multi-objective optimization approach is a set containing many non-dominated solutions or Pareto set. However, only one relevant summary is needed, so a post-Pareto analysis must be performed to reduce this set to a single solution. Several methods have been considered to address this task, including those related to the largest hypervolume, the consensus solution, the shortest distance to the ideal point, and the shortest distance to all points. The approaches have been tested and compared through different experiments, which have been performed using datasets from Document Understanding Conferences (DUC). The methods have been compared with Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The results show that consensus is the method providing the best average values. The results provided by the consensus method improve the ones obtained with the other methods between 10.68% and 27.32% in ROUGE scores.