  We introduce HunSum-1: a dataset for Hungarian abstractive summarization,
consisting of 1.14M news articles. The dataset is built by collecting, cleaning
and deduplicating data from 9 major Hungarian news sites through CommonCrawl.
Using this dataset, we build abstractive summarizer models based on huBERT and
mT5. We demonstrate the value of the created dataset by performing a
quantitative and qualitative analysis on the models' results. The HunSum-1
dataset, all models used in our experiments and our code are available open
source.
