发表机构
University of Illinois Chicago; IIT Bombay（伊利诺伊大学芝加哥分校; 印度理工学院孟买分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于贡献的论文信用评分框架，利用LLM估计局部重要性并通过贡献树和引用图传播，以区分原创与引用贡献，实验验证其有效性。
AI 中文摘要
基于引用的科学影响力度量通常将引用视为统一信号，忽略了被引文献在论文贡献中所扮演的不同角色。我们引入了基于贡献的研究文章信用评分：一种结构化的引用分析，将论文的信用分解为其自身的原创贡献与其所依赖的先前工作。受科学信用的合作博弈视角启发，我们提出了贡献树，这是一个层级框架，在文档结构中保持重要性，并区分原创贡献与引用衍生的贡献。为了使该框架可扩展，我们使用LLM作为局部重要性的噪声比较估计器。我们进一步将模型扩展到文章集合，通过加权引用图传播贡献，从而得到语料库级别的贡献和归一化影响力分数。我们的实验表明，该框架捕获了超越表面启发式的贡献信号。我们的代码可在以下网址获取：this https URL
英文摘要
Citation-based measures of scientific influence typically treat citations as uniform signals, ignoring the different roles that cited works play in a paper's contribution. We introduce contribution-based credit scoring for research articles: a structured citation analysis that decomposes a paper's credit between its own original contribution and the prior work it builds on. Motivated by a cooperative-game view of scientific credit, we propose the contribution tree, a hierarchical framework that conserves importance across the document structure and separates original from citation-derived contribution. To make this framework scalable, we use LLMs as noisy comparative estimators of local importance. We further extend the model to article collections by propagating contributions through weighted citation graphs, yielding corpus-level contributions and normalized influence scores. Our experiments suggest that our framework captures contribution signals beyond surface-level heuristics. Our code is available at https://github.com/sanaebrahimi/Importance_Scoring/
