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.
Liputan6: A Large-scale Indonesian Dataset for Text Summarization
A large Indonesian summarization dataset is introduced, with benchmarking of extractive and abstractive summarization methods using BERT models, including error analysis.
- Year
- 2020
- Venue
- Asian Chapter of the Association for Computational Linguistics 2020
- Authors
- 3
- Hosting
- Abstract onlyARXIV-DEFAULT
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- Abstract & full text
- arxiv.org/abs/2011.00679ARXIV-DEFAULT
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- Semantic Scholar