Dataset for Automatic Summarization of Russian News
This work addresses a gap in resources for Russian language processing, but it is incremental as it primarily introduces a new dataset rather than novel methods.
The authors tackled the lack of a dataset for automatic summarization in Russian by introducing Gazeta, the first dataset for Russian news summarization, and demonstrated its validity by benchmarking extractive and abstractive models, showing that the pretrained mBART model is effective for this task.
Automatic text summarization has been studied in a variety of domains and languages. However, this does not hold for the Russian language. To overcome this issue, we present Gazeta, the first dataset for summarization of Russian news. We describe the properties of this dataset and benchmark several extractive and abstractive models. We demonstrate that the dataset is a valid task for methods of text summarization for Russian. Additionally, we prove the pretrained mBART model to be useful for Russian text summarization.