Document-level Relation Extraction (DocRE) aims to extract relations from multiple sentences simultaneously. Existing graph-based methods adopt static graphs to represent the document structure, which is unable to capture complex interactions. Besides, they take all sentences in the document as the scope of relation extraction (RE) while introducing noise by irrelevant sentences. Furthermore, they do not explicitly model the reasoning chain, leading to a lack of explainability in the reasoning results. These limitations may significantly hinder their performance in practical applications. In this paper, we propose a model based on selective attention and path reasoning for DocRE. Firstly, we adopt hierarchical heterogeneous graph neural networks and recurrent neural networks to realize document modeling and capture complex interactions in the document. Secondly, we adopt selective attention to select sentences related to the entity pair to generate document subgraphs as the scope of RE. Lastly, we adopt path reasoning to explicitly model the reasoning chain between multiple entities in the document subgraph, infer the relations between entities and provide corresponding supporting evidence. Extensive experiment results on three benchmark datasets show that the proposed framework is effective and achieves superior performance compared to most methods. Further analysis demonstrates that selective attention and path reasoning can discover more accurate inter-sentence relations and supporting evidence.

文档级关系提取（DocRE）旨在同时从多个句子中提取关系。现有的基于图的方法采用静态图来表示文档结构，无法捕获复杂的交互。此外，他们将文档中的所有句子作为关系提取（RE）的范围，同时通过不相关的句子引入噪声。此外，它们没有明确地对推理链进行建模，导致推理结果缺乏可解释性。这些限制可能会严重阻碍它们在实际应用中的性能。在本文中，我们提出了一种基于选择性注意和路径推理的 DocRE 模型。首先，我们采用层次异构图神经网络和循环神经网络来实现文档建模并捕获文档中的复杂交互。其次，我们采用选择性注意来选择与实体对相关的句子来生成文档子图作为RE的范围。最后，我们采用路径推理对文档子图中多个实体之间的推理链进行显式建模，推断实体之间的关系并提供相应的支持证据。对三个基准数据集的广泛实验结果表明，与大多数方法相比，所提出的框架是有效的并且实现了优越的性能。进一步分析表明，选择性注意和路径推理可以发现更准确的句子间关系和支持证据。