Toward the Automatic Generation of an Objective Function for Extractive Text Summarization

Abstract

A fitness function is a type of objective function that quantifies the optimality of a solution; the correct formulation of this function is relevant, in evolutionary-based ATS systems, because it must indicate the quality of the summaries. Several unsupervised evolutionary methods for the automatic text summarization (ATS) task proposed in current standards require authors to manually construct an objective function that guides the algorithms to create good-quality summaries. In this sense, it is necessary to test each fitness function created to measure its performance; however, this process is time consuming and only a few functions are analyzed. This study proposes the automatic generation of heuristic functions, through genetic programming (GP), to be applied in the ATS task. Therefore, our proposed method for ATS provides an automatically generated fitness function for cluster-based unsupervised approaches. The results of this study, using two standard collections, demonstrate to automatically obtain an orientation function that leads to good quality abstracts.

Keywords

• Automatic text summarization
• clustering
• genetic programming
• genetic algorithms
• heuristic functions