SumeCzech: Large Czech News-Based Summarization Dataset
Milan Straka, Nikita Mediankin, Tom Kocmi, Zdeněk Žabokrtský, Vojtěch Hudeček, Jan Hajič · 2018
Document summarization is a well-studied NLP task.With the emergence of artificial neural network models, the summarization performance is increasing, as are the requirements on training data.However, only a few datasets are available for Czech, none of them particularly large.Additionally, summarization has been evaluated predominantly on English, with the commonly used ROUGE metric being English-specific.In this paper, we try to address both issues.We present SumeCzech, a Czech news-based summarization dataset.It contains more than a million documents, each consisting of a headline, a several sentences long abstract and a full text.The dataset can be downloaded using the provided scripts available at http://hdl.handle.net/11234/1-2615.We evaluate several summarization baselines on the dataset, including a strong abstractive approach based on Transformer neural network architecture.The evaluation is performed using a language-agnostic variant of ROUGE.