Title: GN-Transformer: Fusing AST and Source Code information in Graph Networks. Abstract: As opposed to natural languages, source code understanding is influenced by grammar relations between tokens regardless of their identifier name. Considering graph representation of source code such as Abstract Syntax Tree (AST) and Control Flow Graph (CFG), can capture a token’s grammatical relationships that are not obvious from the source code. Most existing methods are late fusion and underperform when supplementing the source code text with a graph representation. We propose a novel method called GN-Transformer to fuse representations learned from graph and text modalities under the Graph Networks (GN) framework with attention mechanism. Our method learns the embedding on a constructed graph called Syntax-Code Graph (SCG). We perform experiments on the structure of SCG, an ablation study on the model design and the hyper-paramaters to conclude that the performance advantage is from the fusion method and not the specific details of the model. The proposed method achieved state of the art performance in two code summarization datasets and across three metrics.