  Multi-document summarization (MDS) aims to generate a summary for a number of
related documents. We propose HGSUM, an MDS model that extends an
encoder-decoder architecture, to incorporate a heterogeneous graph to represent
different semantic units (e.g., words and sentences) of the documents. This
contrasts with existing MDS models which do not consider different edge types
of graphs and as such do not capture the diversity of relationships in the
documents. To preserve only key information and relationships of the documents
in the heterogeneous graph, HGSUM uses graph pooling to compress the input
graph. And to guide HGSUM to learn compression, we introduce an additional
objective that maximizes the similarity between the compressed graph and the
graph constructed from the ground-truth summary during training. HGSUM is
trained end-to-end with graph similarity and standard cross-entropy objectives.
Experimental results over MULTI-NEWS, WCEP-100, and ARXIV show that HGSUM
outperforms state-of-the-art MDS models. The code for our model and experiments
is available at: https://github.com/oaimli/HGSum.
