Novelty, akin to gene mutation in evolution, opens possibilities for scientific advancement. Despite peer review being the gold standard for evaluating novelty in scholarly communication and resource allocation, the vast volume of submissions necessitates an automated measure of scientific novelty. Adopting a perspective that views novelty as the atypical combination of existing knowledge, we introduce an information-theoretic measure of novelty in scholarly publications. This measure is quantified by the degree of `surprise' perceived by a language model that represents the distribution of scientific discourse. The proposed measure is accompanied by face and construct validity evidence; the former demonstrates correspondence to scientific common sense, and the latter is endorsed through alignments with novelty evaluations from a select panel of domain experts. Additionally, characterized by its interpretability, fine granularity, and accessibility, this measure addresses gaps prevalent in existing methods. We believe this measure holds great potential to benefit editors, stakeholders, and policymakers, and it provides a confident lens for examining the relationship between novelty and scientific dynamics such as creativity, interdisciplinarity, scientific advances, and more.

翻译：新颖性，犹如进化中的基因突变，为科学进步开辟了可能性。尽管同行评审是评估学术交流和资源配置中新颖性的黄金标准，但海量的投稿量使得自动化衡量科学新颖性成为必要。本文采纳将新颖性视为现有知识非典型组合的视角，提出一种基于信息论的学术论文新颖性度量方法。该度量通过代表科学话语分布的语言模型所感知的"惊奇"程度来量化。我们为这一度量提供了表面效度和结构效度证据：前者展示了其与科学常识的一致性，后者则通过与一组精选领域专家对新颖性的评估进行校准而得到验证。此外，该度量以其可解释性、细粒度和易获取性为特征，弥补了现有方法中的普遍不足。我们相信，这一度量有望为编辑、利益相关者和政策制定者带来巨大裨益，并为审视新颖性与创造力、跨学科性、科学进步等科学动力学之间的关系提供可靠的视角。