# Structured Neural Summarization

arXiv: https://arxiv.org/abs/1811.01824 · HTML: https://mlsanity.com/paper/1811.01824/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2018.

## Built on by (most cited first, 30 of 54)

- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [Retrieval-Augmented Generation for Code Summarization via Hybrid GNN](https://mlsanity.com/paper/2006.05405/) (2020)
- [Language-Agnostic Representation Learning of Source Code from Structure and Context](https://mlsanity.com/paper/2103.11318/) (2021)
- [Stealthy Backdoor Attack for Code Models](https://mlsanity.com/paper/2301.02496/) (2023)
- [On the Evaluation of Neural Code Summarization](https://mlsanity.com/paper/2107.07112/) (2021)
- [Structure-Aware Abstractive Conversation Summarization via Discourse and Action Graphs](https://mlsanity.com/paper/2104.08400/) (2021)
- [Reconstructive Sequence-Graph Network for Video Summarization](https://mlsanity.com/paper/2105.04066/) (2021)
- [Code Structure Guided Transformer for Source Code Summarization](https://mlsanity.com/paper/2104.09340/) (2021)
- [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023)
- [BASS: Boosting Abstractive Summarization with Unified Semantic Graph](https://mlsanity.com/paper/2105.12041/) (2021)
- [VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements](https://mlsanity.com/paper/2112.10893/) (2021)
- [Improving Code Summarization with Block-wise Abstract Syntax Tree Splitting](https://mlsanity.com/paper/2103.07845/) (2021)
- [An Extractive-and-Abstractive Framework for Source Code Summarization](https://mlsanity.com/paper/2206.07245/) (2022)
- [GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Search](https://mlsanity.com/paper/2111.02671/) (2021)
- [Gaussian Processes on Cellular Complexes](https://mlsanity.com/paper/2311.01198/) (2023)
- [Enhancing Scientific Papers Summarization with Citation Graph](https://mlsanity.com/paper/2104.03057/) (2021)
- [HEAT: Hyperedge Attention Networks](https://mlsanity.com/paper/2201.12113/) (2022)
- [Impact of Evaluation Methodologies on Code Summarization](https://mlsanity.com/paper/2108.09619/) (2021)
- [Exploiting Code Symmetries for Learning Program Semantics](https://mlsanity.com/paper/2308.03312/) (2023)
- [Leveraging Locality in Abstractive Text Summarization](https://mlsanity.com/paper/2205.12476/) (2022)
- [HierarchyNet: Learning to Summarize Source Code with Heterogeneous Representations](https://mlsanity.com/paper/2205.15479/) (2022)
- [RACONTEUR: A Knowledgeable, Insightful, and Portable LLM-Powered Shell Command Explainer](https://mlsanity.com/paper/2409.02074/) (2024)
- [Learning Program Semantics with Code Representations: An Empirical Study](https://mlsanity.com/paper/2203.11790/) (2022)
- [MetaTPTrans: A Meta Learning Approach for Multilingual Code Representation Learning](https://mlsanity.com/paper/2206.06460/) (2022)
- [Learning Program Representations with a Tree-Structured Transformer](https://mlsanity.com/paper/2208.08643/) (2022)
- [Generative Subgraph Retrieval for Knowledge Graph-Grounded Dialog Generation](https://mlsanity.com/paper/2410.09350/) (2024)
- [Learning to Generate Code Comments from Class Hierarchies](https://mlsanity.com/paper/2103.13426/) (2021)
- [XFL: Naming Functions in Binaries with Extreme Multi-label Learning](https://mlsanity.com/paper/2107.13404/) (2021)
- [Learning to Describe Solutions for Bug Reports Based on Developer Discussions](https://mlsanity.com/paper/2110.04353/) (2021)
- [TEP-GNN: Accurate Execution Time Prediction of Functional Tests using Graph Neural Networks](https://mlsanity.com/paper/2208.11947/) (2022)

## Abstract (arXiv)

Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input. Based on the promising results of graph neural networks on highly structured data, we develop a framework to extend existing sequence encoders with a graph component that can reason about long-distance relationships in weakly structured data such as text. In an extensive evaluation, we show that the resulting hybrid sequence-graph models outperform both pure sequence models as well as pure graph models on a range of summarization tasks.