CSL : A Large-scale C hinese Scientific Literature Dataset (2022)

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.

## introduction with the increase in the publication of papers, natural language processing (nlp) tools that assist users in writing, searching, and archiving scientific literature have grown increasingly important. for instance, paper/citation recommendation @xcite , topic classification @xcite and summarization @xcite systems have been developed. the construction of these automatic systems primarily relies on academic resources such as largescale corpus @xcite , citation graphs @xcite @xcite and supervised scientific datasets @xcite . these resources, however, are mostly centered around the english language, which restricts the arrow indicates the input and output of the task, for example, "@xmath0. a: abstract; t: title; k: keywords; c: category; d: discipline. the development of techniques for addressing non-english scientific nlp tasks. until recently, progresses in the nlp research for chinese resources and models has lagged behind their english counterparts. to fill the gap of non-english scientific resources and spur the chinese scientific nlp research, in this paper, we introduce csl: a large-scale chinese scientific literature dataset. csl contains 396,209 chinese papers' meta-information, including title, abstract, keywords, academic category and discipline. papers are collected from comprehensive chinese academic journals covering a wide range of distribution. in particular, we divide them into we implement some state-of-the-art chinese text-to-text models and evaluate on the proposed benchmark. we also demonstrate the effectiveness of the csl dataset as pre-training corpus. specifically, we pre-train t5 with paper abstracts, namely csl-t5. it outperforms the model trained on the general-domain corpus, which can be used as a strong baseline for the proposed benchmark. the experiment results show that though existing models can achieve acceptable performance on scientific nlp tasks, it still needs future efforts to reach a practical level. the main contributions of this paper are summarized as follows: ## conclusion this paper presents the first chinese scientific literature dataset, csl, which can serve as a pretraining corpus and can derive abundant nlp tasks. based on csl, we build an evaluation benchmark to explore the challenges posed by automatic analysis of chinese scientific documents. experimental results find difficulties in existing models in the chinese scientific domain and point out the future directions. limitations and future work. in the current version of csl, to provide accurate category/discipline labels, we only use journals focused on one domain, which resulted in some data loss. in future work, we will provide multi-label datasets to cover cross domain papers, and annotate csl with more attributes like chinese-english parallel data for academic machine translation. also, the versatile nlp task derived from csl constitutes a naturally cross-task scenario. in the future, we will explore the role of csl in cross-task and few-shot research.