'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
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