Source　code　summarization　is　the　task　of　generating　a　readable　natural　language　to　describe　the　functionality　of　source　code.　Code　summarization　is　rapidly　expanding,　especially　as　the　research　takes　great　advantage　of　advances　in　neural　networks　and　artificial　intelligence　technologies.　Some　mainstream　methods　input　the　structural　information　(abstract　syntax　tree　(AST))　of　the　source　code　into　the　language　model　to　generate　relatively　satisfactory　comments.　However,　existing　methods　can　not　capture　code＇s　long　dependencies　from　AST　for　effective　code　summarization.　In　this　paper,　we　provide　a　novel　way　to　generate　code　summaries　by　combining　a　graph-based　neural　network　and　a　Transformer-XL　network.　We　utilize　the　graph-based　neural　network　to　better　capture　the　structure　information　of　AST,　and　the　Transformer-XL　network　to　learn　important　tokens　in　the　AST　and　alleviate　the　problem　of　long　dependency.　We　evaluate　our　technique　on　the　standard　Java　dataset.　The　experimental　results　show　that　the　effectiveness　of　our　model　is　remarkable.　It　pushes　the　precision　score　to　60.73%　(5.21%　absolute　improvement)　and　the　F1　score　to　51.06%.