Calculating the Significance of Automatic Extractive Text Summarization using a Genetic Algorithm

Abstract

In the last 16 years with the existence of Document Understanding Conference (DUC), several methods have been developed in Automatic Extractive Text Summarization (AETS) that have allowed the continuous improvement of this task. However, no significant analysis has been performed to determine the significance of the AETS methods. In this paper, we present a new method based on a Genetic Algorithm to determine the best sentence combination of DUC01 and DUC02 datasets to rank the newest methods of AETS. Using three heuristics presented in the state-of-the-art, we rank the most recent AETS methods, obtaining upper bounds and recovering lower bounds of the state-of-the-art.