One key challenge in multi-document summarization is to capture the relations
among input documents that distinguish between single document summarization
(SDS) and multi-document summarization (MDS). Few existing MDS works address
this issue. One effective way is to encode document positional information to
assist models in capturing cross-document relations. However, existing MDS
models, such as Transformer-based models, only consider token-level positional
information. Moreover, these models fail to capture sentences' linguistic
structure, which inevitably causes confusions in the generated summaries.
Therefore, in this paper, we propose document-aware positional encoding and
linguistic-guided encoding that can be fused with Transformer architecture for
MDS. For document-aware positional encoding, we introduce a general protocol to
guide the selection of document encoding functions. For linguistic-guided
encoding, we propose to embed syntactic dependency relations into the
dependency relation mask with a simple but effective non-linear encoding
learner for feature learning. Extensive experiments show the proposed model can
generate summaries with high quality.

本文提出了一种新的多文档自动摘要模型，使用文档关系编码和基于语言指导的编码辅助 Transformer 模型来增强多文档摘要的质量，实验结果表明该模型可以生成高质量的自动摘要。

针对抽象式多文档摘要的文档感知位置编码和语言引导编码

Document-aware Positional Encoding and Linguistic-guided Encoding for Abstractive Multi-document Summarization

Graphs that capture relations between textual units have great benefits for
detecting salient information from multiple documents and generating overall
coherent summaries. In this paper, we develop a neural abstractive
multi-document summarization (MDS) model which can leverage well-known graph
representations of documents such as similarity graph and discourse graph, to
more effectively process multiple input documents and produce abstractive
summaries. Our model utilizes graphs to encode documents in order to capture
cross-document relations, which is crucial to summarizing long documents. Our
model can also take advantage of graphs to guide the summary generation
process, which is beneficial for generating coherent and concise summaries.
Furthermore, pre-trained language models can be easily combined with our model,
which further improve the summarization performance significantly. Empirical
results on the WikiSum and MultiNews dataset show that the proposed
architecture brings substantial improvements over several strong baselines.

本文提出了一种神经抽象的文档多摘要模型，该模型利用文档之间的相似性图和话语图等知名图形表示来更有效地处理多个输入文档并生成抽象的摘要，其利用图形编码文档以捕捉跨文档关系，可以利用图形来指导摘要生成过程，结果表明该架构相比多个强基线带来了实质性的改进。