  In this paper, we introduce a large-scale Indonesian summarization dataset.
We harvest articles from Liputan6.com, an online news portal, and obtain
215,827 document-summary pairs. We leverage pre-trained language models to
develop benchmark extractive and abstractive summarization methods over the
dataset with multilingual and monolingual BERT-based models. We include a
thorough error analysis by examining machine-generated summaries that have low
ROUGE scores, and expose both issues with ROUGE it-self, as well as with
extractive and abstractive summarization models.
