Query-oriented text summarization based on multiobjective evolutionary algorithms and word embeddings

Automatic text summarization systems are nowadays of great help to extract relevant information from large corpora. Many solutions to the task have been proposed from the perspective of the optimization of a single-objective function, aiming at finding the global optimum. This is an unrealistic goal since when multiple objectives are considered a solution that optimizes one of the objectives may induce the opposite effect on the others. Recently other solutions have been proposed that involve multiple, conflicting objectives, but which eventually are aggregated into a scalar function thus resulting in a single-objective optimization problem. Furthermore, oftentimes a typical bag of words model is used and little effort has been made to include semantic relations between sentences to improve performance. In this paper a novel method for query-oriented summarization is proposed as a multiobjective optimization problem taking into account the Pareto front and based on an embedded representation of sentences. The method is evaluated with the TAC 2009 dataset. Experimental results show that the approach contributes to improve performance significantly. To the authors' knowledge, the method is the first attempt to include embedded representations of sentences in a multiobjective optimization solution, which applies the Pareto approach to query-oriented summarization.