  Background: Code summarization automatically generates the corresponding
natural language descriptions according to the input code. Comprehensiveness of
code representation is critical to code summarization task. However, most
existing approaches typically use coarse-grained fusion methods to integrate
multi-modal features. They generally represent different modalities of a piece
of code, such as an Abstract Syntax Tree (AST) and a token sequence, as two
embeddings and then fuse the two ones at the AST/code levels. Such a coarse
integration makes it difficult to learn the correlations between fine-grained
code elements across modalities effectively. Aims: This study intends to
improve the model's prediction performance for high-quality code summarization
by accurately aligning and fully fusing semantic and syntactic structure
information of source code at node/token levels. Method: This paper proposes a
Multi-Modal Fine-grained Feature Fusion approach (MMF3) for neural code
summarization. We introduce a novel fine-grained fusion method, which allows
fine-grained fusion of multiple code modalities at the token and node levels.
Specifically, we use this method to fuse information from both token and AST
modalities and apply the fused features to code summarization. Results: We
conduct experiments on one Java and one Python datasets, and evaluate generated
summaries using four metrics. The results show that: 1) the performance of our
model outperforms the current state-of-the-art models, and 2) the ablation
experiments show that our proposed fine-grained fusion method can effectively
improve the accuracy of generated summaries. Conclusion: MMF3 can mine the
relationships between crossmodal elements and perform accurate fine-grained
element-level alignment fusion accordingly. As a result, more clues can be
provided to improve the accuracy of the generated code summaries.
