Peringkasan dokumen berita bahasa indonesia menggunakan metode cross latent semantic analysis

Abstract

Summarizing news documents in Bahasa serves to find main ideas or any other important information from a piece of news. A system to extract the information from ones consisting of many paragraphs is then deemed necessary in order to present precise main ideas or important information to the readers without them having to read the entire passage of news documents, in addition to become useful for Really Simple Syndication Feed (RSS-Feed). This article discusses summarizing news documents in Bahasa using Cross Latent Semantic Analysis (CLSA). To test if the summary resulted from CLSA qualified, this study examines 240 news articles retrieved from www.kompas.com and employs two experts from different fields. The summary resulted from CLSA with a compression rate of 30% obtains an F-Measure of 0,72%. This study also evidently indicates that CLSA has better performance from Latent Semantic Analysis (LSA) which was the initial system for CLSA, despite both F-Measure percentages being only slightly different.