Beyond Citations: Integrating Finding-Based Relations for Improved Biomedical Article Representations

High-quality scientific article embeddings are essential for tasks like document retrieval, citation recommendation, and classification.Traditional citation-based approaches assume citations reflect semantic similarity-an assumption that introduces bias and noise.Recent models like SciNCL and SPECTER2 have attempted to refine citation-based representations but still struggle with noisy citation edges and fail to fully leverage textual information.To address these limitations, we propose a hybrid approach that combines Finding-Citation Graphs (FCG) with contrastive learning.Our method improves triplet selection by filtering out less important citations and incorporating finding similarity relations, leading to better semantic relationship capture.Evaluated on the SciRepEval benchmark, our approach consistently outperforms citation-only baselines, showing the value of text-based semantic structures.While we do not surpass state-of-the-art models in most tasks, our results reveal the limitations of purely citation-based embeddings and suggest paths for improvement through enhanced semantic integration and domain-specific adaptations.