Abstract
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 itself, as well as with extractive and abstractive summarization models.
Cite
CITATION STYLE
Koto, F., Lau, J. H., & Baldwin, T. (2020). Liputan6: A Large-scale Indonesian Dataset for Text Summarization. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, AACL-IJCNLP 2020 (pp. 598–608). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.aacl-main.60
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