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

IndoSum: A New Benchmark Dataset for Indonesian Text Summarization