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.- Anthology ID:
- 2020.aacl-main.60
- Volume:
- 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
- Month:
- December
- Year:
- 2020
- Address:
- Suzhou, China
- Venue:
- AACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 598–608
- Language:
- URL:
- https://aclanthology.org/2020.aacl-main.60
- DOI:
- Cite (ACL):
- Fajri Koto, Jey Han Lau, and Timothy Baldwin. 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, pages 598–608, Suzhou, China. Association for Computational Linguistics.
- Cite (Informal):
- Liputan6: A Large-scale Indonesian Dataset for Text Summarization (Koto et al., AACL 2020)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2020.aacl-main.60.pdf
- Code
- fajri91/sum_liputan6
- Data
- Liputan6, IndoSum, LCSTS