Breakthrough research in scientific fields usually comes as a manifestation of major development and advancement. These advances build to an epiphany where new ways of thinking about a problem become possible. Identifying breakthrough research can be useful for cultivating and funding further innovation. This article presents a new method for identifying scientific breakthroughs from research papers based on cue words commonly associated with major advancements. We looked for specific terms signifying scientific breakthroughs in citing sentences to identify breakthrough articles. By setting a threshold for the number of citing sentences (“citances”) with breakthrough cue words that peer scholars often use when evaluating research, we identified articles containing breakthrough research. We call this approach the “others-evaluation” process. We then shortlisted candidates from the selected articles based on the authors’ evaluations of their own research, found in the abstracts. This we call the “self-evaluation” process. Combining the two approaches into a dual “others-self” evaluation process, we arrived at a sample of 237 potential breakthrough articles, most of which are recommended by the Faculty Opinions. Based on the breakthrough articles identified, using SVM, TextCNN, and BERT to train the models to identify abstracts with breakthrough evaluations. This automatic identification model can greatly simplify the process of others-self-evaluation process and promote identifying breakthrough research.

科学领域的突破性研究通常是重大发展和进步的体现。这些进步造就了一个顿悟，使人们有可能以新的方式思考问题。确定突破性研究对培养和资助进一步的创新可能是有用的。本文提出了一种新的方法，该方法可以根据与重大进步通常相关的提示词从研究论文中识别出科学突破。我们在引用句子以识别突破性文章的过程中寻找了表示科学突破的特定术语。通过设置同行学者在评估研究时经常使用的带有突破性提示词的引用句子（“引用”）的数量阈值，我们确定了包含突破性研究的文章。我们称这种方法为“其他评估”过程。然后，我们根据摘要中作者对自己研究的评估，从选定的文章中筛选出候选人。我们称之为“自我评估”过程。将这两种方法结合成一个双重的“他人－自己”评估过程，我们得出了237篇潜在突破性文章的样本，其中大多数是《学院意见》所推荐的。基于识别出的突破性文章，使用SVM，TextCNN和BERT训练模型以识别具有突破性评估的摘要。这种自动识别模型可以极大地简化他人的自我评估过程，并促进识别突破性研究。将这两种方法结合成一个双重的“他人－自己”评估过程，我们得出了237篇潜在突破性文章的样本，其中大多数是《学院意见》所推荐的。基于识别出的突破性文章，使用SVM，TextCNN和BERT训练模型以识别具有突破性评估的摘要。这种自动识别模型可以极大地简化他人的自我评估过程，并促进识别突破性研究。将这两种方法结合成一个双重的“他人－自己”评估过程，我们得出了237篇潜在突破性文章的样本，其中大多数是《学院意见》所推荐的。基于识别出的突破性文章，使用SVM，TextCNN和BERT训练模型以识别具有突破性评估的摘要。这种自动识别模型可以极大地简化他人的自我评估过程，并促进识别突破性研究。