Automatic Text Summarization with Categorization on Online News About Indonesian Public Figures Using Fuzzy Logic Method

Abstract

Mass media has a main purpose to provide actual information and lifestyle in society. For that purpose, mass media has been developed into many forms with online news as one of them. With so many news articles on the internet, it's been difficult for readers to get information about an event or a person in a short time and efficiently. Text summarization is one solution to save time and improve efficiency by shortening the document into a shorter version but containing important information from the source document. This research’s goal is to create an automatic text summarization in several news articles in mass media that have been categorized regarding Indonesian public figures using Fuzzy Logic approach. Automatic text summarization can be used in a variety of news articles which are structured texts with neat grammar to help users find the desired information from the many news articles that are available. The result showed automatic text summarization with categorization could provide summarized news articles that are easy to understand, contain the main information of the article, grouped into matched categories and public figure, and worked efficiency.