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 this https URL

本研究介绍了 CSL，一个包含 39.6 万篇中国科技文献的大型数据集，为中国自然语言处理和监督学习提供了有意义的语料库和标注。同时，本研究还基于 CSL 提出了一个基准用于评估模型在科技领域任务中的表现，如摘要生成、关键词提取和文本分类，并分析已有 NLP 模型在这些任务上的表现，揭示了进行中文科技 NLP 任务所面临的挑战。