Since the abstract can be found at the beginning of most scientific articles and is an essential part of the article, several attempts have been made to explore the rhetorical moves of abstracts in various research fields. These studies dealt only with accepted articles since they can be easily accessed. Although the findings of such works have some pedagogical implications for academic writing courses for young researchers who are relatively new to their fields, they do not contribute enough to the transparency of the peer review processes conducted in research fields. Increasing transparency requires considering rejected articles since they help to clarify the decision criteria in the peer review. Based on 591 abstracts of accepted or rejected articles submitted to Journal of Visualization (JOV), the present study aimed at exploring the differences between the accepted and rejected abstracts. The results show that there are significant differences in the structures of the abstracts. Since we also successfully develop a classification model for the decision using a machine-learning technique, the findings of this study have some implications for developing a semi-automatic reviewing system that can reduce the reviewer’s burden and increase the review quality.Graphical abstract

中文翻译：

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JOV摘要的可视化

由于摘要可以在大多数科学文章的开头找到并且是文章的重要组成部分，因此已经进行了几次尝试来探索各个研究领域的摘要的修辞动作。这些研究仅涉及已接受的文章，因为它们很容易访问。尽管这些工作的发现对年轻研究人员的学术写作课程具有一些教学意义，这些研究人员对他们的领域相对较新，但它们对研究领域中同行评审过程的透明度的贡献不够。提高透明度需要考虑被拒绝的文章，因为它们有助于澄清同行评审中的决策标准。基于提交给 Journal of Visualization (JOV) 的 591 篇接受或拒绝文章的摘要，本研究旨在探讨被接受和被拒绝的摘要之间的差异。结果表明，摘要的结构存在显着差异。由于我们还使用机器学习技术成功开发了决策分类模型，因此本研究的结果对于开发可以减轻审稿人负担并提高审稿质量的半自动审稿系统具有一定的意义。图形摘要