Liputan6: A Large-scale Indonesian Dataset for Text Summarization
This provides a valuable resource for Indonesian NLP research, but it is incremental as it adapts existing methods to a new dataset.
The authors tackled the lack of a large-scale Indonesian text summarization dataset by creating Liputan6, which includes 215,827 document-summary pairs from an online news portal, and they developed benchmark methods using pre-trained language models, achieving results with ROUGE scores as part of their analysis.
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.