Liputan6: A Large-scale Indonesian Dataset for Text Summarization

Fajri Koto, Jey Han Lau, Timothy Baldwin


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:
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2020.aacl-main.60.pdf
Code
 fajri91/sum_liputan6
Data
Liputan6IndoSumLCSTS