Before revision:  In this work, we gather the best of both encoding strategies, proposing novel models that encode an input graph combining both global and local node contexts . Our approaches are able to learn better contextualized node embeddings for text generation .
After revision:  In this work, we gather the best of both encoding strategies, proposing novel models that encode an input graph combining both global and local node contexts , in order to learn better contextualized node embeddings for text generation .