An automatic text summarization using text features and singular value decomposition for popular articles in Indonesia language

Abstract

The machine text summarization is of necessary with the existing of the enormous amount of popular articles. This work evaluates the latent semantic analysis technique to summarize popular articles in Indonesia language. The summarization performance are evaluated with respect to precision, recall, and F-measure. As results, the performance seems to be reasonably high particularly when the summarization level is 50%.