  Multi-Document Scientific Summarization (MDSS) aims to produce coherent and
concise summaries for clusters of topic-relevant scientific papers. This task
requires precise understanding of paper content and accurate modeling of
cross-paper relationships. Knowledge graphs convey compact and interpretable
structured information for documents, which makes them ideal for content
modeling and relationship modeling. In this paper, we present KGSum, an MDSS
model centred on knowledge graphs during both the encoding and decoding
process. Specifically, in the encoding process, two graph-based modules are
proposed to incorporate knowledge graph information into paper encoding, while
in the decoding process, we propose a two-stage decoder by first generating
knowledge graph information of summary in the form of descriptive sentences,
followed by generating the final summary. Empirical results show that the
proposed architecture brings substantial improvements over baselines on the
Multi-Xscience dataset.
