Affiliations:
Universidad Autónoma del Estado de México, Unidad Académica Profesional Tianguistenco, Instituto Literario, CP, Toluca, Edo. Mex, México
Correspondence:
[*]
Corresponding authors. Jonathan Rojas Simón, Yulia Ledeneva and René Arnulfo García-Hernández, Universidad Autónoma del Estado de México, Unidad Académica Profesional Tianguistenco, Instituto Literario No. 100, CP 50000, Toluca, Edo. Mex, México. E-mails: ids_jonathan_rojas@hotmail.com (J.R. Simón), yledeneva@yahoo.com (Y. Ledeneva), renearnulfo@ hotmail.com (R.A. García-Hernández).
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.
Keywords: Significance, Topline, text summarization, genetic algorithm, upper bounds
