Indonesian News Extractive Text Summarization Using Latent Semantic Analysis

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.