Training summarization models requires substantial amounts of training data.
However for less resourceful languages like Hungarian, openly available models
and datasets are notably scarce. To address this gap our paper introduces
HunSum-2 an open-source Hungarian corpus suitable for training abstractive and
extractive summarization models. The dataset is assembled from segments of the
Common Crawl corpus undergoing thorough cleaning, preprocessing and
deduplication. In addition to abstractive summarization we generate
sentence-level labels for extractive summarization using sentence similarity.
We train baseline models for both extractive and abstractive summarization
using the collected dataset. To demonstrate the effectiveness of the trained
models, we perform both quantitative and qualitative evaluation. Our dataset,
models and code are publicly available, encouraging replication, further
research, and real-world applications across various domains.

训练摘要模型需要大量的训练数据。然而，对于像匈牙利语这样资源有限的语言，公开可用的模型和数据集明显稀缺。为了填补这个空白，我们的论文介绍了 HunSum-2，这是一个开源的匈牙利语语料库，适用于训练抽象和提取式摘要模型。该数据集经过了详细的清洗、预处理和去重。除了抽象摘要外，我们还使用句子相似度生成句子级标签，用于提取式摘要。我们使用收集到的数据集训练了提取式和抽象式摘要的基线模型。为了证明训练模型的有效性，我们进行了定量和定性评估。我们的数据集、模型和代码都是公开可用的，鼓励复制、进一步研究以及在各个领域进行实际应用。