CSL: A Large-scale Chinese Scientific Literature Dataset

Yudong Li, Yuqing Zhang, Zhe Zhao, Linlin Shen, Weijie Liu, Weiquan Mao, Hui Zhang

Abstract
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 will be publicly available.
Anthology ID:
2022.coling-1.344
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
3917–3923
Language:
URL:
https://aclanthology.org/2022.coling-1.344
DOI:
Bibkey:
Cite (ACL):
Yudong Li, Yuqing Zhang, Zhe Zhao, Linlin Shen, Weijie Liu, Weiquan Mao, and Hui Zhang. 2022. CSL: A Large-scale Chinese Scientific Literature Dataset. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3917–3923, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
CSL: A Large-scale Chinese Scientific Literature Dataset (Li et al., COLING 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.coling-1.344.pdf
Code
 ydli-ai/csl +  additional community code
Data
CSL-2022
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