  Automatic text summarization is generally considered as a challenging task in
the NLP community. One of the challenges is the publicly available and large
dataset that is relatively rare and difficult to construct. The problem is even
worse for low-resource languages such as Indonesian. In this paper, we present
IndoSum, a new benchmark dataset for Indonesian text summarization. The dataset
consists of news articles and manually constructed summaries. Notably, the
dataset is almost 200x larger than the previous Indonesian summarization
dataset of the same domain. We evaluated various extractive summarization
approaches and obtained encouraging results which demonstrate the usefulness of
the dataset and provide baselines for future research. The code and the dataset
are available online under permissive licenses.
