# Automatic Source Code Summarization with Extended Tree-LSTM

arXiv: https://arxiv.org/abs/1906.08094 · HTML: https://mlsanity.com/paper/1906.08094/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2019.

## Built on by (most cited first, 28 of 28)

- [Retrieval Augmented Code Generation and Summarization](https://mlsanity.com/paper/2108.11601/) (2021)
- [Is ChatGPT the Ultimate Programming Assistant -- How far is it?](https://mlsanity.com/paper/2304.11938/) (2023)
- [Automatic Code Summarization via ChatGPT: How Far Are We?](https://mlsanity.com/paper/2305.12865/) (2023)
- [Code Structure Guided Transformer for Source Code Summarization](https://mlsanity.com/paper/2104.09340/) (2021)
- [CodeQA: A Question Answering Dataset for Source Code Comprehension](https://mlsanity.com/paper/2109.08365/) (2021)
- [A Survey on Machine Learning Techniques for Source Code Analysis](https://mlsanity.com/paper/2110.09610/) (2021)
- [Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code](https://mlsanity.com/paper/2205.11739/) (2022)
- [An Extractive-and-Abstractive Framework for Source Code Summarization](https://mlsanity.com/paper/2206.07245/) (2022)
- [Source Code Summarization with Structural Relative Position Guided Transformer](https://mlsanity.com/paper/2202.06521/) (2022)
- [M2TS: Multi-Scale Multi-Modal Approach Based on Transformer for Source Code Summarization](https://mlsanity.com/paper/2203.09707/) (2022)
- [API2Com: On the Improvement of Automatically Generated Code Comments Using API Documentations](https://mlsanity.com/paper/2103.10668/) (2021)
- [ESALE: Enhancing Code-Summary Alignment Learning for Source Code Summarization](https://mlsanity.com/paper/2407.01646/) (2024)
- [Learning Program Semantics with Code Representations: An Empirical Study](https://mlsanity.com/paper/2203.11790/) (2022)
- [Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization](https://mlsanity.com/paper/2305.11074/) (2023)
- [GypSum: Learning Hybrid Representations for Code Summarization](https://mlsanity.com/paper/2204.12916/) (2022)
- [Abstract Syntax Tree for Programming Language Understanding and Representation: How Far Are We?](https://mlsanity.com/paper/2312.00413/) (2023)
- [PSIMiner: A Tool for Mining Rich Abstract Syntax Trees from Code](https://mlsanity.com/paper/2103.12778/) (2021)
- [Database Workload Characterization with Query Plan Encoders](https://mlsanity.com/paper/2105.12287/) (2021)
- [A Survey of Deep Learning Models for Structural Code Understanding](https://mlsanity.com/paper/2205.01293/) (2022)
- [Comparative Code Structure Analysis using Deep Learning for Performance Prediction](https://mlsanity.com/paper/2102.07660/) (2021)
- [A Survey on Natural Language Processing for Programming](https://mlsanity.com/paper/2212.05773/) (2022)
- [Large Language Models for Code Summarization](https://mlsanity.com/paper/2405.19032/) (2024)
- [APIContext2Com: Code Comment Generation by Incorporating Pre-Defined API Documentation](https://mlsanity.com/paper/2303.01645/) (2023)
- [Evaluating the Impact of Source Code Parsers on ML4SE Models](https://mlsanity.com/paper/2206.08713/) (2022)
- [DeepCodeProbe: Towards Understanding What Models Trained on Code Learn](https://mlsanity.com/paper/2407.08890/) (2024)
- [Tadashi: Enabling AI-Based Automated Code Generation With Guaranteed Correctness](https://mlsanity.com/paper/2410.03210/) (2024)
- [Bringing Structure to Naturalness: On the Naturalness of ASTs](https://mlsanity.com/paper/2504.08234/) (2025)
- [Enhancing Neural Code Representation with Additional Context](https://mlsanity.com/paper/2510.12082/) (2025)

## Abstract (arXiv)

Neural machine translation models are used to automatically generate a document from given source code since this can be regarded as a machine translation task. Source code summarization is one of the components for automatic document generation, which generates a summary in natural language from given source code. This suggests that techniques used in neural machine translation, such as Long Short-Term Memory (LSTM), can be used for source code summarization. However, there is a considerable difference between source code and natural language: Source code is essentially em structured, having loops and conditional branching, etc. Therefore, there is some obstacle to apply known machine translation models to source code. Abstract syntax trees (ASTs) capture these structural properties and play an important role in recent machine learning studies on source code. Tree-LSTM is proposed as a generalization of LSTMs for tree-structured data. However, there is a critical issue when applying it to ASTs: It cannot handle a tree that contains nodes having an arbitrary number of children and their order simultaneously, which ASTs generally have such nodes. To address this issue, we propose an extension of Tree-LSTM, which we call Multi-way Tree-LSTM and apply it for source code summarization. As a result of computational experiments, our proposal achieved better results when compared with several state-of-the-art techniques.