Scientific influence measured by original and cited contributions
From Citations to Contributions: LLM-Assisted Credit Scoring of Research Articles
Digital LibrariesArtificial IntelligenceComputation and LanguageInformation Retrieval
Summary
Scientific papers usually get credit based on how many times they are cited, but this treats all citations the same. The researchers introduce a new way to measure how much a paper contributes by separating its own new ideas from the work it builds on. They use a special system called a contribution tree and large language models to estimate the importance of different parts of a paper. This method can also measure influence across many papers by looking at how contributions flow through citation networks. Their experiments show this approach finds meaningful signals that simple citation counts miss.
citationscientific influencecontribution treelarge language modelscredit scoringcitation graphcooperative game theoryimportance estimationresearch impact
Authors
Sana Ebrahimi, Suraj Shetiya, Abolfazl Asudeh
Abstract
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/