  Graphs are a highly expressive data structure, but it is often difficult for
humans to find patterns from a complex graph. Hence, generating
human-interpretable sequences from graphs have gained interest, called
graph2seq learning. It is expected that the compositionality in a graph can be
associated to the compositionality in the output sequence in many graph2seq
tasks. Therefore, applying compositionality-aware GNN architecture would
improve the model performance. In this study, we adopt the multi-level
attention pooling (MLAP) architecture, that can aggregate graph representations
from multiple levels of information localities. As a real-world example, we
take up the extreme source code summarization task, where a model estimate the
name of a program function from its source code. We demonstrate that the model
having the MLAP architecture outperform the previous state-of-the-art model
with more than seven times fewer parameters than it.
