IndoSum: A New Benchmark Dataset for Indonesian Text Summarization
This work addresses the problem of data scarcity for low-resource languages like Indonesian in NLP, but it is incremental as it primarily introduces a new dataset rather than novel methods.
The authors tackled the lack of a large public dataset for Indonesian text summarization by introducing IndoSum, a new benchmark dataset that is nearly 200 times larger than previous datasets in the same domain, and they evaluated extractive summarization methods to provide baseline results.
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.