Affiliations: Universidad Autónoma del Estado de México, Unidad Académica Profesional Tianguistenco, Instituto Literario, Toluca, Edo. Mex, México
Correspondence:
[*]
Corresponding author. Yulia Ledeneva, Autonomous University of the State of Mexico, Instituto Literario No. 100, CP 50000, Toluca, State of Mexico, Mexico. E-mail: yledeneva@yahoo.com.
Abstract: The methods of Automatic Extractive Summarization (AES) uses the features of the sentences of the original text to extract the most important information that will be considered in summary. It is known that the first sentences of the text are more relevant than the rest of the text (this heuristic is called baseline), so the position of the sentence (in reverse order) is used to determine its relevance, which means that the last sentences have practically no possibility of being selected. In this paper, we present a way to soften the importance of sentences according to the position. The comprehensive tests were done on one of the best AES methods using the bag of words and n-grams models with the with DUC02 and DUC01 data sets to determine the importance of sentences.
Keywords: Automatic Text Summarization, n-gram Model, bag of words model, slope calculation, genetic algorithm
