# M2TS: Multi-Scale Multi-Modal Approach Based on Transformer for Source Code Summarization arXiv: https://arxiv.org/abs/2203.09707 ยท HTML: https://mlsanity.com/paper/2203.09707/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Builds on 25 of its 59 references that are in the map. - First posted on arXiv 18 Mar 2022. ## Builds on (most cited first, 25 of 25) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016) - [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014) - [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014) - [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020) - [code2seq: Generating Sequences from Structured Representations of Code](https://mlsanity.com/paper/1808.01400/) (2018) - [A Transformer-based Approach for Source Code Summarization](https://mlsanity.com/paper/2005.00653/) (2020) - [M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection](https://mlsanity.com/paper/2104.09770/) (2021) - [Improving Automatic Source Code Summarization via Deep Reinforcement Learning](https://mlsanity.com/paper/1811.07234/) (2018) - [Improved Code Summarization via a Graph Neural Network](https://mlsanity.com/paper/2004.02843/) (2020) - [A Neural Model for Generating Natural Language Summaries of Program Subroutines](https://mlsanity.com/paper/1902.01954/) (2019) - [Language-Agnostic Representation Learning of Source Code from Structure and Context](https://mlsanity.com/paper/2103.11318/) (2021) - [TreeGen: A Tree-Based Transformer Architecture for Code Generation](https://mlsanity.com/paper/1911.09983/) (2019) - [A parallel corpus of Python functions and documentation strings for automated code documentation and code generation](https://mlsanity.com/paper/1707.02275/) (2017) - [Recommendations for Datasets for Source Code Summarization](https://mlsanity.com/paper/1904.02660/) (2019) - [Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks](https://mlsanity.com/paper/1804.00823/) (2018) - [Retrieve and Refine: Exemplar-based Neural Comment Generation](https://mlsanity.com/paper/1910.10419/) (2019) - [Code Structure Guided Transformer for Source Code Summarization](https://mlsanity.com/paper/2104.09340/) (2021) - [Automatic Source Code Summarization with Extended Tree-LSTM](https://mlsanity.com/paper/1906.08094/) (2019) - [CoCoSum: Contextual Code Summarization with Multi-Relational Graph Neural Network](https://mlsanity.com/paper/2107.01933/) (2021) - [Multi-Scale Self-Attention for Text Classification](https://mlsanity.com/paper/1912.00544/) (2019) - [A Multi-Modal Transformer-based Code Summarization Approach for Smart Contracts](https://mlsanity.com/paper/2103.07164/) (2021) - [Retrieve and Refine: Exemplar-based Neural Comment Generation](https://mlsanity.com/paper/2010.04459/) (2020) - [Exploiting Method Names to Improve Code Summarization: A Deliberation Multi-Task Learning Approach](https://mlsanity.com/paper/2103.11448/) (2021) ## Built on by (most cited first, 14 of 14) - [CWEval: Outcome-driven Evaluation on Functionality and Security of LLM Code Generation](https://mlsanity.com/paper/2501.08200/) (2025) - [Using Large Language Models to Generate JUnit Tests: An Empirical Study](https://mlsanity.com/paper/2305.00418/) (2023) - [SALLM: Security Assessment of Generated Code](https://mlsanity.com/paper/2311.00889/) (2023) - [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023) - [An Extractive-and-Abstractive Framework for Source Code Summarization](https://mlsanity.com/paper/2206.07245/) (2022) - [FRANC: A Lightweight Framework for High-Quality Code Generation](https://mlsanity.com/paper/2307.08220/) (2023) - [A Large-Scale Exploit Instrumentation Study of AI/ML Supply Chain Attacks in Hugging Face Models](https://mlsanity.com/paper/2410.04490/) (2024) - [The Fault in our Stars: Quality Assessment of Code Generation Benchmarks](https://mlsanity.com/paper/2404.10155/) (2024) - [ESALE: Enhancing Code-Summary Alignment Learning for Source Code Summarization](https://mlsanity.com/paper/2407.01646/) (2024) - [Can Developers Prompt? A Controlled Experiment for Code Documentation Generation](https://mlsanity.com/paper/2408.00686/) (2024) - [Abstract Syntax Tree for Programming Language Understanding and Representation: How Far Are We?](https://mlsanity.com/paper/2312.00413/) (2023) - [The Role of Transformer Models in Advancing Blockchain Technology: A Systematic Survey](https://mlsanity.com/paper/2409.02139/) (2024) - [Assessing the Software Security Comprehension of Large Language Models](https://mlsanity.com/paper/2512.21238/) (2025) - [Revisiting the Role of Natural Language Code Comments in Code Translation](https://mlsanity.com/paper/2601.16661/) (2026) ## Abstract (arXiv) Source code summarization aims to generate natural language descriptions of code snippets. Many existing studies learn the syntactic and semantic knowledge of code snippets from their token sequences and Abstract Syntax Trees (ASTs). They use the learned code representations as input to code summarization models, which can accordingly generate summaries describing source code. Traditional models traverse ASTs as sequences or split ASTs into paths as input. However, the former loses the structural properties of ASTs, and the latter destroys the overall structure of ASTs. Therefore, comprehensively capturing the structural features of ASTs in learning code representations for source code summarization remains a challenging problem to be solved. In this paper, we propose M2TS, a Multi-scale Multi-modal approach based on Transformer for source code Summarization. M2TS uses a multi-scale AST feature extraction method, which can extract the structures of ASTs more completely and accurately at multiple local and global levels. To complement missing semantic information in ASTs, we also obtain code token features, and further combine them with the extracted AST features using a cross modality fusion method that not only fuses the syntactic and contextual semantic information of source code, but also highlights the key features of each modality. We conduct experiments on two Java and one Python datasets, and the experimental results demonstrate that M2TS outperforms current state-of-the-art methods. We release our code at https://github.com/TranSMS/M2TS.