# Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs

arXiv: https://arxiv.org/abs/2001.11003 · HTML: https://mlsanity.com/paper/2001.11003/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2020.

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

- [A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT](https://mlsanity.com/paper/2303.04226/) (2023)
- [JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs](https://mlsanity.com/paper/2106.10502/) (2021)
- [Structural Adapters in Pretrained Language Models for AMR-to-text Generation](https://mlsanity.com/paper/2103.09120/) (2021)
- [FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations](https://mlsanity.com/paper/2204.06508/) (2022)
- [Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models](https://mlsanity.com/paper/2106.01623/) (2021)
- [GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation](https://mlsanity.com/paper/2204.06674/) (2022)
- [Generative De Novo Protein Design with Global Context](https://mlsanity.com/paper/2204.10673/) (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)
- [Innovations in Neural Data-to-text Generation: A Survey](https://mlsanity.com/paper/2207.12571/) (2022)
- [Mind the Labels: Describing Relations in Knowledge Graphs With Pretrained Models](https://mlsanity.com/paper/2210.07373/) (2022)
- [Graph Language Models](https://mlsanity.com/paper/2401.07105/) (2024)
- [Generating Faithful Text From a Knowledge Graph with Noisy Reference Text](https://mlsanity.com/paper/2308.06488/) (2023)
- [A Systematic Review of Data-to-Text NLG](https://mlsanity.com/paper/2402.08496/) (2024)
- [Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation](https://mlsanity.com/paper/2109.03808/) (2021)
- [CATS: A Pragmatic Chinese Answer-to-Sequence Dataset with Large Scale and High Quality](https://mlsanity.com/paper/2306.11477/) (2023)
- [How to Train Your Agent to Read and Write](https://mlsanity.com/paper/2101.00916/) (2021)
- [Curriculum-Based Self-Training Makes Better Few-Shot Learners for Data-to-Text Generation](https://mlsanity.com/paper/2206.02712/) (2022)
- [Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning](https://mlsanity.com/paper/2308.05317/) (2023)
- [GraSAME: Injecting Token-Level Structural Information to Pretrained Language Models via Graph-guided Self-Attention Mechanism](https://mlsanity.com/paper/2404.06911/) (2024)
- [MGSA: Multi-Granularity Graph Structure Attention for Knowledge Graph-to-Text Generation](https://mlsanity.com/paper/2409.10294/) (2024)
- [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)
- [Knowledge Graph for NLG in the context of conversational agents](https://mlsanity.com/paper/2307.01548/) (2023)
- [Controlling Topic-Focus Articulation in Meaning-to-Text Generation using Graph Neural Networks](https://mlsanity.com/paper/2310.02053/) (2023)
- [TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copy](https://mlsanity.com/paper/2402.01714/) (2024)
- [Probing Omissions and Distortions in Transformer-based RDF-to-Text Models](https://mlsanity.com/paper/2409.16707/) (2024)
- [Evaluating and Improving Graph to Text Generation with Large Language Models](https://mlsanity.com/paper/2501.14497/) (2025)

## Abstract (arXiv)

Recent graph-to-text models generate text from graph-based data using either global or local aggregation to learn node representations. Global node encoding allows explicit communication between two distant nodes, thereby neglecting graph topology as all nodes are directly connected. In contrast, local node encoding considers the relations between neighbor nodes capturing the graph structure, but it can fail to capture long-range relations. In this work, we gather both encoding strategies, proposing novel neural models which encode an input graph combining both global and local node contexts, in order to learn better contextualized node embeddings. In our experiments, we demonstrate that our approaches lead to significant improvements on two graph-to-text datasets achieving BLEU scores of 18.01 on AGENDA dataset, and 63.69 on the WebNLG dataset for seen categories, outperforming state-of-the-art models by 3.7 and 3.1 points, respectively.