Abstract Purpose To uncover the evaluation information on the academic contribution of research papers cited by peers based on the content cited by citing papers, and to provide an evidence-based tool for evaluating the academic value of cited papers. Design/methodology/approach CiteOpinion uses a deep learning model to automatically extract citing sentences from representative citing papers; it starts with an analysis on the citing sentences, then it identifies major academic contribution points of the cited paper, positive/negative evaluations from citing authors and the changes in the subjects of subsequent citing authors by means of Recognizing Categories of Moves (problems, methods, conclusions, etc.), and sentiment analysis and topic clustering. Findings Citing sentences in a citing paper contain substantial evidences useful for academic evaluation. They can also be used to objectively and authentically reveal the nature and degree of contribution of the cited paper reflected by citation, beyond simple citation statistics. Practical implications The evidence-based evaluation tool CiteOpinion can provide an objective and in-depth academic value evaluation basis for the representative papers of scientific researchers, research teams, and institutions. Originality/value No other similar practical tool is found in papers retrieved. Research limitations There are difficulties in acquiring full text of citing papers. There is a need to refine the calculation based on the sentiment scores of citing sentences. Currently, the tool is only used for academic contribution evaluation, while its value in policy studies, technical application, and promotion of science is not yet tested.

中文翻译：

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CiteOpinion：基于引用句的基于证据的研究论文学术贡献评估工具

摘要目的根据被引论文的引用内容，发现同行评议论文学术贡献的评价信息，为评价被引论文的学术价值提供依据。设计/方法/方法CiteOpinion使用深度学习模型从代表性的引文中自动提取引文句子。首先从对被引句子的分析开始，然后通过识别动作类别（问题，方法），确定被引论文的主要学术贡献点，被引作者的正面/负面评价以及后续被引作者主题的变化。 ，结论等），以及情感分析和主题聚类。调查结果引用论文中的引用句子包含大量证据，可用于学术评估。除了简单的引文统计，它们还可以用于客观真实地揭示被引文反映的被引论文的性质和程度。实际意义循证评估工具CiteOpinion可为科学研究人员，研究团队和机构的代表论文提供客观而深入的学术价值评估基础。原创性/价值在检索的论文中找不到其他类似的实用工具。研究局限性很难获得引用论文的全文。需要基于引用句子的情感分数来改进计算。目前，该工具仅用于学术贡献评估，