TL;DR
CiteFuncRanker establishes a robust and interpretable ranking-based paradigm for bibliometric research by capturing the nuanced and context-dependent relative preferences between citation roles, and advance citation analysis beyond categorical classification toward a more context-aware and semantically grounded understanding of scholarly communication.
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 nor...
A joint multi-task learning (Joint-MTL) framework that simultaneously models citation intent classification and citation evaluation classification through a shared encoder with task-specific prediction heads is proposed, contributing to more comprehensive citation context analysis.
The results show that combining temporal, lexical relevance, and self-citation signals produces publication rankings that consistently differ from traditional citation- and recency-based methods while providing interpretable approach for examining multiple aspects of scientific publications.
This hybrid method provides a reproducible, scalable, open-source approach for automated cluster assessment in SLRs and will be implemented in the EmbedSLR open-source software.
Impact-DPO integrates temporally informed prompting with direct preference optimization, enabling LLMs to learn comparative influence patterns without explicit graph message passing, and formalizes citation forecasting as pairwise preference learning on temporal text-attributed graphs.
Results demonstrate that the proposed ranking-aware distillation mechanism significantly enhances scientific text representation quality while maintaining efficient inference, offering a more effective contrastive learning method for domain-specific retrieval tasks.
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