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Deep Graph Convolutional Encoders for Structured Data to Text Generation

arXiv 1810.09995 ↗First posted 23 Oct 2018cs.CLcited by 27

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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.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Oct 2018.

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Citing papers per year: 2021: 10; 2022: 8; 2023: 6; 2024: 1; 2025: 2.

27 papers.

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  1. 2021BertGCN: Transductive Text Classification by Combining GCN and BERTcited by 74
  2. 2023Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answeringcited by 64
  3. 2021JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphscited by 58
  4. 2021Text-to-Table: A New Way of Information Extractioncited by 47
  5. 2021Structural Information Preserving for Graph-to-Text Generationcited by 24
  6. 2021Improving Compositional Generalization with Self-Training for Data-to-Text Generationcited by 19
  7. 2022GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generationcited by 16
  8. 2023Evaluating Generative Models for Graph-to-Text Generationcited by 16
  9. 2025Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learningcited by 12
  10. 2021Stage-wise Fine-tuning for Graph-to-Text Generationcited by 11
  11. 2023ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translationcited by 11
  12. 2024MatSciRE: Leveraging Pointer Networks to Automate Entity and Relation Extraction for Material Science Knowledge-base Constructioncited by 11
15 more
  1. 2021Graphine: A Dataset for Graph-aware Terminology Definition Generationcited by 10
  2. 2022Innovations in Neural Data-to-text Generation: A Surveycited by 10
  3. 2022Multi-objective Deep Data Generation with Correlated Property Controlcited by 10
  4. 2021AGGGEN: Ordering and Aggregating while Generatingcited by 7
  5. 2023Localize, Retrieve and Fuse: A Generalized Framework for Free-Form Question Answering over Tablescited by 6
  6. 2023Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text Generationcited by 5
  7. 2022INFINITY: A Simple Yet Effective Unsupervised Framework for Graph-Text Mutual Conversioncited by 3
  8. 2022Graph-to-Text Generation with Dynamic Structure Pruningcited by 2
  9. 2021Generating Diverse Descriptions from Semantic Graphscited by 1
  10. 2021Triples-to-Text Generation with Reinforcement Learning Based Graph-augmented Neural Networkscited by 1
  11. 2022Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistencycited by 1
  12. 2022Compositionality-Aware Graph2Seq Learningcited by 0
  13. 2022Mapping Process for the Task: Wikidata Statements to Text as Wikipedia Sentencescited by 0
  14. 2023Improving Primary Healthcare Workflow Using Extreme Summarization of Scientific Literature Based on Generative AIcited by 0
  15. 2025SAFT: Structure-Aware Fine-Tuning of LLMs for AMR-to-Text Generationcited by 0