mlsanityData to 14 Oct 2026

AST-Transformer: Encoding Abstract Syntax Trees Efficiently for Code Summarization

arXiv 2112.01184 ↗First posted 2 Dec 2021cs.CL cs.SEcited by 11

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Abstract (arXiv)

Code summarization aims to generate brief natural language descriptions for source code. As source code is highly structured and follows strict programming language grammars, its Abstract Syntax Tree (AST) is often leveraged to inform the encoder about the structural information. However, ASTs are usually much longer than the source code. Current approaches ignore the size limit and simply feed the whole linearized AST into the encoder. To address this problem, we propose AST-Transformer to efficiently encode tree-structured ASTs. Experiments show that AST-Transformer outperforms the state-of-arts by a substantial margin while being able to reduce 90~95% of the computational complexity in the encoding process.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 10 of its 16 references that are in the map. First posted on arXiv 2 Dec 2021.

Builds on

10 of 16 references.

References · 10

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
  3. 2018Self-Attention with Relative Position Representationscited by 1,120
  4. 2018code2seq: Generating Sequences from Structured Representations of Codecited by 179
  5. 2020A Transformer-based Approach for Source Code Summarizationcited by 133
  6. 2018Improving Automatic Source Code Summarization via Deep Reinforcement Learningcited by 100
  7. 2016A Convolutional Attention Network for Extreme Summarization of Source Codecited by 91
  8. 2020Code Prediction by Feeding Trees to Transformerscited by 70
  9. 2019Code Generation as a Dual Task of Code Summarizationcited by 62
  10. 2016Tree-to-Sequence Attentional Neural Machine Translationcited by 28

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 4; 2025: 2; 2026: 1.

11 papers.

  1. 2023xPQA: Cross-Lingual Product Question Answering across 12 Languagescited by 10
  2. 2024EyeTrans: Merging Human and Machine Attention for Neural Code Summarizationcited by 9
  3. 2023Automating Code-Related Tasks Through Transformers: The Impact of Pre-trainingcited by 8
  4. 2023A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Software Engineering Taskscited by 5
  5. 2024InternLM-Law: An Open Source Chinese Legal Large Language Modelcited by 5
  6. 2022Diverse Title Generation for Stack Overflow Posts with Multiple Sampling Enhanced Transformercited by 2
  7. 2024MESIA: Understanding and Leveraging Supplementary Nature of Method-level Comments for Automatic Comment Generationcited by 2
  8. 2024FuncEvalGMN: Evaluating Functional Correctness of SQL via Graph Matching Networkcited by 1
  9. 2025Bringing Structure to Naturalness: On the Naturalness of ASTscited by 1
  10. 2025Issue Retrieval and Verification Enhanced Supplementary Code Comment Generationcited by 0
  11. 2026Learning Sparse Decision Trees via Transformer Variational Auto-Encoderscited by 0