HunSum-1: an Abstractive Summarization Dataset for Hungarian
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 34 references that are in the map. First posted on arXiv 1 Feb 2023.
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References · 19
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- 2019Text Summarization with Pretrained Encoderscited by 553
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- 2016FastText.zip: Compressing text classification modelscited by 331
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- 2016SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documentscited by 245
- 2019Leveraging Pre-trained Checkpoints for Sequence Generation Taskscited by 182
- 2018Bottom-Up Abstractive Summarizationcited by 177
- 2019Fine-tune BERT for Extractive Summarizationcited by 122
- 2016Neural Summarization by Extracting Sentences and Wordscited by 114
- 2018Neural Document Summarization by Jointly Learning to Score and Select Sentencescited by 56
- 2022HuSpaCy: an industrial-strength Hungarian natural language processing toolkitcited by 8
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