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.
@article{arxiv.2011.00679,
title = {Liputan6: A Large-scale Indonesian Dataset for Text Summarization},
author = {Fajri Koto and Jey Han Lau and Timothy Baldwin},
journal= {arXiv preprint arXiv:2011.00679},
year = {2020}
}