  Code summarization (CS) is becoming a promising area in recent language
understanding, which aims to generate sensible human language automatically for
programming language in the format of source code, serving in the most
convenience of programmer developing. It is well known that programming
languages are highly structured. Thus previous works attempt to apply
structure-based traversal (SBT) or non-sequential models like Tree-LSTM and
graph neural network (GNN) to learn structural program semantics. However, it
is surprising that incorporating SBT into advanced encoder like Transformer
instead of LSTM has been shown no performance gain, which lets GNN become the
only rest means modeling such necessary structural clue in source code. To
release such inconvenience, we propose structure-induced Transformer, which
encodes sequential code inputs with multi-view structural clues in terms of a
newly-proposed structure-induced self-attention mechanism. Extensive
experiments show that our proposed structure-induced Transformer helps achieve
new state-of-the-art results on benchmarks.
