## 2 Related Work
### Document-Level Relation Extraction  
Document-level relation extraction aims to extract relations between entities within a single document. Existing methods can be categorized into three approaches: evidence-based methods, graph-based methods, and attention-based methods. [PERSON] et al. [6] proposed the EIDER framework, which improves document-level relation extraction by extracting and integrating key evidence sentences. EIDER adopts an architecture that combines joint relation extraction with a lightweight evidence extraction model and incorporates a hybrid layer for reasoning fusion. On the other hand, [PERSON] et al. [5] introduced the Sentence Importance Estimation and Focusing (SIEF) framework to address the insufficient robustness of existing document-level relation extraction models. SIEF effectively guides the model to focus on key evidence sentences by estimating sentence importance and introducing a focusing loss function. [PERSON] et al. [15] then proposed DREAM, the first self-training method for evidence retrieval in document-level relation extraction. This method addresses two key challenges in evidence retrieval: excessive model memory consumption and scarcity of annotated data. Evidence-based methods offer the following advantages: (1) By focusing on evidence sentences most directly related to the relation, they reduce interference from noise and irrelevant information, thereby improving extraction accuracy [16]. (2) These methods typically provide clear indications of which text segments serve as evidence for extracting specific relations, ensuring high interpretability. Their main limitation lies in their heavy dependence on the quality of selected evidence sentences, where missing or misjudging critical evidence significantly impacts the extraction results. For graph-based approaches, CGM2 IR [17] introduced two techniques to enhance document-level relation extraction: context-guided weighted mention integration and pairwise inference on entity-pair graphs to extract relation interdependencies. GAIN [18] is a graph aggregation and inference network with a dual graph structure, constructing a heterogeneous mention-level graph (hMG) and an entity-level graph (EG) for feature propagation and relation reasoning. The hMG models interactions between mentions, while the EG handles relation reasoning between entities. The recently proposed CorefDRE [19] represents multi-sentence features by incorporating core reference information into dynamically constructed heterogeneous graphs and introduces a mention-pronoun core reference resolution to compute pronoun affinity, along with a noise suppression mechanism to mitigate pronoun-induced ambiguity. Graph-based methods have the following advantages: (1) Graph structures provide models with a global perspective, facilitating understanding and reasoning of complex relations between entities. (2) They can utilize structured information within documents, such as inter-sentence connections and entity co-occurrences. Their main disadvantage is the complexity of graph structure construction and computation, making them challenging to apply to large-scale documents. Attention-based methods typically utilize attention scores to capture contextual information and employ adaptive regularization techniques to enhance model generalization. For example, [PERSON] et al. [20] presented a semi-supervised framework featuring axial attention modules, adaptive loss functions, and knowledge distillation capabilities. SSAN [5] incorporates unique structural dependencies between mention pairs into self-attention mechanisms and encoding stages, including two transformation modules in each self-attention block to generate adaptively regulated attention biases. Dense-CCNet [21] is a densely connected cross-cross-attention network for DocRE that employs cross-cross-attention (CCA) to bidirectionally collect contextual information on entity pair matrices and densely connects layers to capture single-hop and multi-hop reasoning features.