  Two crucial issues for text summarization to generate faithful summaries are
to make use of knowledge beyond text and to make use of cross-sentence
relations in text. Intuitive ways for the two issues are Knowledge Graph (KG)
and Graph Neural Network (GNN) respectively. Entities are semantic units in
text and in KG. This paper focuses on both issues by leveraging entities
mentioned in text to connect GNN and KG for summarization. Firstly, entities
are leveraged to construct a sentence-entity graph with weighted multi-type
edges to model sentence relations, and a relational heterogeneous GNN for
summarization is proposed to calculate node encodings. Secondly, entities are
leveraged to link the graph to KG to collect knowledge. Thirdly, entities guide
a two-step summarization framework defining a multi-task selector to select
salient sentences and entities, and using an entity-focused abstractor to
compress the sentences. GNN is connected with KG by constructing
sentence-entity graphs where entity-entity edges are built based on KG,
initializing entity embeddings on KG, and training entity embeddings using
entity-entity edges. The relational heterogeneous GNN utilizes both edge
weights and edge types in GNN to calculate graphs with weighted multi-type
edges. Experiments show the proposed method outperforms extractive baselines
including the HGNN-based HGNNSum and abstractive baselines including the
entity-driven SENECA on CNN/DM, and outperforms most baselines on NYT50.
Experiments on sub-datasets show the density of sentence-entity edges greatly
influences the performance of the proposed method. The greater the density, the
better the performance. Ablations show effectiveness of the method.
