  In this paper, we proposed a multi-document summarization system using
semantic role labeling (SRL) and semantic graph for Indonesian news articles.
In order to improve existing summarizer, our system modified summarizer that
employed subject, predicate, object, and adverbial (SVOA) extraction for
predicate argument structure (PAS) extraction. SVOA extraction is replaced with
SRL model for Indonesian. We also replace the genetic algorithm to identify
important PAS with the decision tree classifier since the summarizer without
genetic algorithm gave better performance. The decision tree model is employed
to identify important PAS. The decision tree model with 10 features achieved
better performance than decision tree with 4 sentence features. Experiments and
evaluations are conducted to generate 100 words summary and 200 words summary.
The evaluation shows the proposed model get 0.313 average ROUGE-2 recall in 100
words summary and 0.394 average ROUGE-2 recall in 200 words summary.
