From Citations to Contributions: LLM-Assisted Credit Scoring of Research Articles
TL;DR
The contribution tree is proposed, a hierarchical framework that conserves importance across the document structure and separates original from citation-derived contribution, and is extended to article collections by propagating contributions through weighted citation graphs, yielding corpus-level contributions and normalized influence scores.
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/
