Most people need textual or visual interfaces in order to make sense of Semantic Web data. In this paper, we investigate the problem of generating natural language summaries for Semantic Web data using neural networks. Our end-to-end trainable architecture encodes the information from a set of triples into a vector of fixed dimensionality and generates a textual summary by conditioning the output on the encoded vector. We explore a set of different approaches that enable our models to verbalise entities from the input set of triples in the generated text. Our systems are trained and evaluated on two corpora of loosely aligned Wikipedia snippets with triples from DBpedia and Wikidata, with promising results.

大多数人需要文本或视觉界面才能理解语义Web数据。在本文中，我们研究了使用神经网络为语义Web数据生成自然语言摘要的问题。我们的端到端可训练体系结构将一组三元组中的信息编码为固定维数的向量，并通过对编码向量上的输出进行调节来生成文本摘要。我们探索了一组不同的方法，这些方法使我们的模型能够从生成的文本中的三元组输入中对实体进行口头表达。我们的系统在两类松散对齐的Wikipedia片段上进行了训练和评估，这些片段具有DBpedia和Wikidata的三元组，并取得了可喜的结果。