  In this paper, we develop a neural summarization model which can effectively
process multiple input documents and distill Transformer architecture with the
ability to encode documents in a hierarchical manner. We represent
cross-document relationships via an attention mechanism which allows to share
information as opposed to simply concatenating text spans and processing them
as a flat sequence. Our model learns latent dependencies among textual units,
but can also take advantage of explicit graph representations focusing on
similarity or discourse relations. Empirical results on the WikiSum dataset
demonstrate that the proposed architecture brings substantial improvements over
several strong baselines.
