Liputan6: A Large-scale Indonesian Dataset for Text Summarization

Fajri Koto, Jey Han Lau, Timothy Baldwin
The University of Melbourne


In this paper, we introduce a large-scale Indonesian summarization dataset. We harvest articles from, 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 itself, as well as with extractive and abstractive summarization models.