Improvement of Cluster Importance Algorithm with Sentence Position for News Summarization

Abstract

Text summarization is one of the ways to reduce large document dimension to obtain important information from the document. News is one of information which usually has several sub-topics from a topic. In order to get the main information from a topic as fast as possible, multi-document summarization is the solution, but sometimes it can create redundancy. In this study, we used cluster importance algorithm by considering sentence position to overcome the redundancy. Stages of cluster importance algorithm are sentence clustering, cluster ordering, and selection of sentence representative which will be explained in the subsections below. The contribution of this research was to add the position of sentence in the selection phase of representative sentence. For evaluation, we used 30 topics of Indonesian news tested by using ROUGE-1, there were 2 news topics that had different ROUGE-1 score between using cluster importance algorithm by considering sentence position and using cluster importance. However, those 2 news topics which used cluster importance by considering sentence position have a greater score of Rouge-1 than the one which only used cluster importance. The use of sentence position had an effect on the order of sentence on each topic, but there were only 2 news topics that affected the outcome of the summary.