  Scientific literature serves as a high-quality corpus, supporting a lot of
Natural Language Processing (NLP) research. However, existing datasets are
centered around the English language, which restricts the development of
Chinese scientific NLP. In this work, we present CSL, a large-scale Chinese
Scientific Literature dataset, which contains the titles, abstracts, keywords
and academic fields of 396k papers. To our knowledge, CSL is the first
scientific document dataset in Chinese. The CSL can serve as a Chinese corpus.
Also, this semi-structured data is a natural annotation that can constitute
many supervised NLP tasks. Based on CSL, we present a benchmark to evaluate the
performance of models across scientific domain tasks, i.e., summarization,
keyword generation and text classification. We analyze the behavior of existing
text-to-text models on the evaluation tasks and reveal the challenges for
Chinese scientific NLP tasks, which provides a valuable reference for future
research. Data and code are available at https://github.com/ydli-ai/CSL
