Dataset Card for CSL

Dataset Description

CSL is the Chinese Scientific Literature Dataset.

• Paper: https://aclanthology.org/2022.coling-1.344
• Repository: https://github.com/ydli-ai/CSL

Dataset Summary

The dataset contains titles, abstracts, keywords of papers written in Chinese from several academic fields.

Languages

• Chinese
• English (translation)

Dataset Structure

Data Instances

Data Fields

• doc_id : unique identifier for this document
• title : title of the paper
• abstract : abstract of the paper
• keywords : keywords associated with the paper
• category : the broad category of the paper
• category_eng : English translaction of the broad category (e.g., Engineering)
• discipline : academic discipline of the paper
• discipline_eng : English translation of the academic discipline (e.g., Agricultural Engineering)

The en_translation contains documents translated from Google Translation service. All text are in English, so the fields category_eng and discipline_eng are omitted.

Dataset Usage

Using 🤗 Datasets:

from datasets import load_dataset

dataset = load_dataset('neuclir/csl')['csl']

License & Citation

This dataset is based off the Chinese Scientific Literature Dataset under Apache 2.0. The primay change is the addition of doc_id s, English translactions of the category and discipline descriptions by a native speaker, and basic de-duplication. Code that performed this modification is avalable in this repository .

If you use this data, please cite:

@inproceedings{li-etal-2022-csl,
    title = "{CSL}: A Large-scale {C}hinese Scientific Literature Dataset",
    author = "Li, Yudong  and
      Zhang, Yuqing  and
      Zhao, Zhe  and
      Shen, Linlin  and
      Liu, Weijie  and
      Mao, Weiquan  and
      Zhang, Hui",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2022.coling-1.344",
    pages = "3917--3923",
}