  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.
