Abstract
Identifying the strengths and limitations of a research paper is a core component of any literature review. However, traditional summaries reflect only the authors' self-presented perspective. Analyzing how other researchers discuss and cite the paper can offer a deeper, more practical understanding of its contributions and shortcomings. In this research, we introduce SECite, a novel approach for evaluating scholarly impact through sentiment analysis of citation contexts. We develop a semi-automated pipeline to extract citations referencing nine research papers and apply advanced natural language processing (NLP) techniques with unsupervised machine learning to classify these citation statements as positive or negative. Beyond sentiment classification, we use generative AI to produce sentiment-specific summaries that capture the strengths and limitations of each target paper, derived both from clustered citation groups and from the full text. Our findings reveal meaningful patterns in how the academic community perceives these works, highlighting areas of alignment and divergence between external citation feedback and the authors' own presentation. By integrating citation sentiment analysis with LLM-based summarization, this study provides a comprehensive framework for assessing scholarly contributions.
Abstract (translated)
识别研究论文的优缺点是文献综述的核心组成部分。然而，传统的摘要只能反映作者自身的观点。分析其他研究人员如何讨论和引用该论文可以提供更深层次、更具实践意义的理解其贡献与不足之处。在这项研究中，我们介绍了SECite，这是一种通过引文语境中的情感分析来评估学术影响力的新型方法。我们开发了一种半自动管道，用于提取九篇目标论文的引用，并采用先进的自然语言处理（NLP）技术和无监督机器学习技术将这些引文声明分类为正面或负面评价。 除了情感分类之外，我们还使用生成式人工智能根据聚类引文组和全文本信息来制作特定情感摘要，捕捉每篇目标论文的优点与不足。我们的研究结果揭示了学术界如何看待这些作品的有意义模式，突出了外部引用反馈与作者自身表述之间的共识与分歧领域。 通过将引文情感分析与大型语言模型（LLM）生成的总结相结合，本研究为评估学术贡献提供了一个全面的框架。
URL
https://arxiv.org/abs/2601.07939
