Document-level relation extraction (DocRE) involves identifying relations
between entities distributed in multiple sentences within a document. Existing
methods focus on building a heterogeneous document graph to model the internal
structure of an entity and the external interaction between entities. However,
there are two drawbacks in existing methods. On one hand, anaphor plays an
important role in reasoning to identify relations between entities but is
ignored by these methods. On the other hand, these methods achieve
cross-sentence entity interactions implicitly by utilizing a document or
sentences as intermediate nodes. Such an approach has difficulties in learning
fine-grained interactions between entities across different sentences,
resulting in sub-optimal performance. To address these issues, we propose an
Anaphor-Assisted (AA) framework for DocRE tasks. Experimental results on the
widely-used datasets demonstrate that our model achieves a new state-of-the-art
performance.

我们提出了一种基于指代辅助的框架用于文档级关系抽取任务，实验结果表明我们的模型取得了最先进的性能。

照应辅助的文档级关系抽取

Anaphor Assisted Document-Level Relation Extraction

As a crucial step in extractive document summarization, learning
cross-sentence relations has been explored by a plethora of approaches. An
intuitive way is to put them in the graph-based neural network, which has a
more complex structure for capturing inter-sentence relationships. In this
paper, we present a heterogeneous graph-based neural network for extractive
summarization (HeterSumGraph), which contains semantic nodes of different
granularity levels apart from sentences. These additional nodes act as the
intermediary between sentences and enrich the cross-sentence relations.
Besides, our graph structure is flexible in natural extension from a
single-document setting to multi-document via introducing document nodes. To
our knowledge, we are the first one to introduce different types of nodes into
graph-based neural networks for extractive document summarization and perform a
comprehensive qualitative analysis to investigate their benefits. The code will
be released on Github

本文介绍了一种异构图神经网络 (HeterSumGraph) 用于信息抽取式文档摘要的任务中，该网络结构包含不同粒度级别的语义节点，这些额外的节点作为句子之间的中介，丰富交叉句关系，同时我们的图结构在自然扩展从单个文档设置到 多文档，引入文档节点。该方法是第一次在基于图的神经网络中引入不同类型的节点进行信息抽取式文档摘要，并进行了全面的定性分析以探究其优劣。