News text is a text that contains important
information that is happening to be disseminated to the public. In
the news, the more information, the more text is displayed. Of
course, it takes a lot of time to read the entire text of the news.
Automatic text summarization is needed to help readers
understand the content of the news text quickly. In this study, the
application of the latent semantic analysis method with the
GongLiu, Steinberger Jezek, and Cross techniques will be applied
to automatic text summarization. The test data will be tested by
using local news about politics. By comparing the rate the three
methods previously mentioned, Gongliu is considered the best
amongst the three methods since it has the highest Rogue value
and the fastest processing time.
Keywords: text summarization, semantic analysis, comparing rate