What is it about?
Code summarization automatically generates the corresponding natural language descriptions according to the input code. 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.
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Why is it important?
It can achieve fine-grained matching and fusion between Token modal information and AST modal information, enabling the model to learn detailed associations between the two modalities. It will make readers aware of the effectiveness of fine-grained fusion methods in the field of code summarization.
Perspectives
I hope this article provides a new perspective on code summarization and other areas.
Zheng Ma
Shandong Normal University
Read the Original
This page is a summary of: MMF3: Neural Code Summarization Based on Multi-Modal Fine-Grained Feature Fusion, September 2022, ACM (Association for Computing Machinery),
DOI: 10.1145/3544902.3546251.
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