通过分析引用语境情感，揭示论文真实学术影响。
SECite: Analyzing and Summarizing Citations in Software Engineering Literature
- 从引用文本中提取情感倾向，自动判断论文被认可或质疑程度。
- 用生成式AI提炼出每篇论文的优缺点摘要，覆盖9篇研究论文。
- 帮助研究人员客观评估论文价值，适合文献综述与学术评价场景。
识别研究论文的优势与局限是文献综述的核心。传统摘要仅反映作者自我陈述，而分析他人引用该论文的方式，可提供更深入、实际的贡献与不足认知。本研究提出SECite，一种基于引用上下文情感分析的学术影响力评估新方法。我们构建半自动化流程，提取9篇研究论文的引用，并利用先进的自然语言处理技术与无监督机器学习，将引用语句分类为正面或负面。除情感分类外，还使用生成式AI生成针对不同情感类别的摘要，内容源自聚类后的引用组及全文。研究发现学术界对该类工作的认知存在明显模式，揭示了外部引用反馈与作者自述之间的共识与分歧。结合引用情感分析与大模型摘要，本研究提供了一个全面评估学术贡献的框架。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。