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.

翻译：文本摘要生成忠实摘要的两个关键问题在于利用文本之外的知识以及利用文本中的跨句关系。解决这两个问题的直观方法分别是知识图谱和图神经网络。实体是文本和知识图谱中的语义单元。本文通过利用文本中提及的实体连接图神经网络和知识图谱来进行摘要，从而聚焦于这两个问题。首先，利用实体构建带有加权多类型边的句子-实体图以建模句子关系，并提出一种用于摘要的关系异构图神经网络来计算节点编码。其次，利用实体将该图链接到知识图谱以收集知识。第三，实体引导一个两步摘要框架，该框架定义了一个多任务选择器来选择显著的句子和实体，并使用一个以实体为中心的抽象器来压缩句子。通过构建句子-实体图（其中实体-实体边基于知识图谱构建）、在知识图谱上初始化实体嵌入以及利用实体-实体边训练实体嵌入，将图神经网络与知识图谱连接起来。关系异构图神经网络利用图神经网络中的边权重和边类型来计算带加权多类型边的图。实验表明，所提出的方法在CNN/DM数据集上优于包括基于HGNN的HGNNSum在内的抽取式基线和包括实体驱动的SENECA在内的生成式基线，并且在NYT50数据集上优于大多数基线。在子数据集上的实验表明，句子-实体边的密度对所提出方法的性能有很大影响。密度越大，性能越好。消融实验证明了该方法的有效性。