# Deep Graph Convolutional Encoders for Structured Data to Text Generation

arXiv: https://arxiv.org/abs/1810.09995 · HTML: https://mlsanity.com/paper/1810.09995/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2018.

## Built on by (most cited first, 27 of 27)

- [BertGCN: Transductive Text Classification by Combining GCN and BERT](https://mlsanity.com/paper/2105.05727/) (2021)
- [Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answering](https://mlsanity.com/paper/2309.11206/) (2023)
- [JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs](https://mlsanity.com/paper/2106.10502/) (2021)
- [Text-to-Table: A New Way of Information Extraction](https://mlsanity.com/paper/2109.02707/) (2021)
- [Structural Information Preserving for Graph-to-Text Generation](https://mlsanity.com/paper/2102.06749/) (2021)
- [Improving Compositional Generalization with Self-Training for Data-to-Text Generation](https://mlsanity.com/paper/2110.08467/) (2021)
- [GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation](https://mlsanity.com/paper/2204.06674/) (2022)
- [Evaluating Generative Models for Graph-to-Text Generation](https://mlsanity.com/paper/2307.14712/) (2023)
- [Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning](https://mlsanity.com/paper/2501.01124/) (2025)
- [Stage-wise Fine-tuning for Graph-to-Text Generation](https://mlsanity.com/paper/2105.08021/) (2021)
- [ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation](https://mlsanity.com/paper/2305.16585/) (2023)
- [MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Construction](https://mlsanity.com/paper/2401.09839/) (2024)
- [Graphine: A Dataset for Graph-aware Terminology Definition Generation](https://mlsanity.com/paper/2109.04018/) (2021)
- [Innovations in Neural Data-to-text Generation: A Survey](https://mlsanity.com/paper/2207.12571/) (2022)
- [Multi-objective Deep Data Generation with Correlated Property Control](https://mlsanity.com/paper/2210.01796/) (2022)
- [AGGGEN: Ordering and Aggregating while Generating](https://mlsanity.com/paper/2106.05580/) (2021)
- [Localize, Retrieve and Fuse: A Generalized Framework for Free-Form Question Answering over Tables](https://mlsanity.com/paper/2309.11049/) (2023)
- [Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text Generation](https://mlsanity.com/paper/2311.09467/) (2023)
- [INFINITY: A Simple Yet Effective Unsupervised Framework for Graph-Text Mutual Conversion](https://mlsanity.com/paper/2209.10754/) (2022)
- [Graph-to-Text Generation with Dynamic Structure Pruning](https://mlsanity.com/paper/2209.07258/) (2022)
- [Generating Diverse Descriptions from Semantic Graphs](https://mlsanity.com/paper/2108.05659/) (2021)
- [Triples-to-Text Generation with Reinforcement Learning Based Graph-augmented Neural Networks](https://mlsanity.com/paper/2111.10545/) (2021)
- [Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistency](https://mlsanity.com/paper/2207.00719/) (2022)
- [Compositionality-Aware Graph2Seq Learning](https://mlsanity.com/paper/2201.12178/) (2022)
- [Mapping Process for the Task: Wikidata Statements to Text as Wikipedia Sentences](https://mlsanity.com/paper/2210.12659/) (2022)
- [Improving Primary Healthcare Workflow Using Extreme Summarization of Scientific Literature Based on Generative AI](https://mlsanity.com/paper/2307.15715/) (2023)
- [SAFT: Structure-Aware Fine-Tuning of LLMs for AMR-to-Text Generation](https://mlsanity.com/paper/2507.13381/) (2025)

## Abstract (arXiv)

Most previous work on neural text generation from graph-structured data relies on standard sequence-to-sequence methods. These approaches linearise the input graph to be fed to a recurrent neural network. In this paper, we propose an alternative encoder based on graph convolutional networks that directly exploits the input structure. We report results on two graph-to-sequence datasets that empirically show the benefits of explicitly encoding the input graph structure.