  Previous works on knowledge-to-text generation take as input a few RDF
triples or key-value pairs conveying the knowledge of some entities to generate
a natural language description. Existing datasets, such as WIKIBIO, WebNLG, and
E2E, basically have a good alignment between an input triple/pair set and its
output text. However, in practice, the input knowledge could be more than
enough, since the output description may only cover the most significant
knowledge. In this paper, we introduce a large-scale and challenging dataset to
facilitate the study of such a practical scenario in KG-to-text. Our dataset
involves retrieving abundant knowledge of various types of main entities from a
large knowledge graph (KG), which makes the current graph-to-sequence models
severely suffer from the problems of information loss and parameter explosion
while generating the descriptions. We address these challenges by proposing a
multi-graph structure that is able to represent the original graph information
more comprehensively. Furthermore, we also incorporate aggregation methods that
learn to extract the rich graph information. Extensive experiments demonstrate
the effectiveness of our model architecture.
