## 2 Related Work
### Cross-Document Relation Extraction  
Cross-document relation extraction approaches, on the other hand, aim to extract relations between entities across multiple documents. There are four main categories.  
Information retrieval and context filtering methods focus on retrieving relevant information across documents. ECRIM [8] introduces an entity-based document context filter and cross-path entity relation attention mechanism. MR.COD [22] implements multi-hop evidence retrieval through path mining and ranking. These methods efficiently reduce irrelevant information but depend heavily on search quality.  
Learning-based reasoning methods employ machine learning for this task. REIC [11] is the first learning-based sentence selector for CodRE that extracts sentences based on relation evidence. PILOT [12] utilizes explicit clue information to enhance reasoning paths between entities. These approaches excel at modeling complex relations but require substantial computational resources and data.  
Graph-based approaches model entity relationships across documents. [PERSON] et al. [13] represent input bags as unified entity graphs incorporating non-bridge entities and apply graph reasoning networks (GRNs) with a debiasing strategy to refine prediction distributions. [PERSON] et al. [10] propose a local-to-global causal reasoning (LGCR) network that uses local causal estimation to distinguish confusing information and causality-guided global reasoning to filter irrelevant information in cross-document settings. These methods effectively capture complex relationships but face challenges in graph construction.  
Knowledge-enhanced methods incorporate external knowledge to improve extraction performance. [PERSON] et al. [14] leverage domain knowledge for cross-document relation extraction, providing explanatory text for predictions that enhances interpretability. This approach demonstrates how domain knowledge can guide the extraction process but may be limited by the availability and quality of knowledge resources.  
Cross-Document Event Coreference Resolution (CD-ECR) detects and collects event mentions across different documents, which is also related to this work. [PERSON] et al. [23] proposed a collaborative CD-ECR approach using general large language models (LLMs) for initial event summarization and specialized domain-specific language models (SLMs) during fine-tuning to refine event representations. [PERSON] and [PERSON] [24] introduced a simpler framework for CDEC on ECB+ that achieved state-of-the-art performance with fewer features, using only event trigger word annotations, making the simplified model directly comparable with reference models. Through using fewer features or simplified frameworks, simplified and collaborative learning methods are easier to understand and implement.  
In summary, we identified three major challenges in current cross-document information extraction: (1) Suboptimal input construction that introduces noise and overlooks semantic connections; (2) insufficient inter-document information interaction, with models treating document paths as independent samples; and (3) inadequate discrimination between positive and negative instances in multi-instance scenarios. Our work addresses these limitations through relation-aware context filtering that considers both entity and relation semantics, Path Distance-Weighted Attention that models document path relationships, and cross-path relation matrix mechanisms that enhance discrimination between positive and negative instances.