HunSum-1: an Abstractive Summarization Dataset for Hungarian
This provides a domain-specific resource for Hungarian natural language processing, but it is incremental as it applies existing methods to new data.
The authors introduced HunSum-1, a dataset of 1.14 million Hungarian news articles for abstractive summarization, and built models based on huBERT and mT5 to demonstrate its value through quantitative and qualitative analysis.
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.