A Graph-to-Sequence Model for AMR-to-Text Generation

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Improving Event Detection with Abstract Meaning Representation

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FORGe at SemEval-2017 Task 9: Deep sentence generation based on a sequence of graph transducers

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RIGOTRIO at SemEval-2017 Task 9: Combining Machine Learning and Grammar Engineering for AMR Parsing and Generation

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Graph Neural Networks for Natural Language Processing: A Survey

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Text Generation from Knowledge Graphs with Graph Transformers

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Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward

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Semantic Neural Machine Translation Using AMR

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Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning

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A survey of graph neural networks in various learning paradigms: methods, applications, and challenges

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Investigating Typed Syntactic Dependencies for Targeted Sentiment Classification Using Graph Attention Neural Network

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Deep Learning on Graphs

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One SPRING to Rule Them Both: Symmetric AMR Semantic Parsing and Generation without a Complex Pipeline

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Modeling Graph Structure in Transformer for Better AMR-to-Text Generation

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SQL-to-Text Generation with Graph-to-Sequence Model

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Structural Neural Encoders for AMR-to-text Generation

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Improving Abstractive Dialogue Summarization with Graph Structures and Topic Words

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Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem

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

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Graph Neural Networks for Natural Language Processing: A SurveyPreprint

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Graph Pre-training for AMR Parsing and Generation

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AMR-To-Text Generation with Graph Transformer

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Graph-based Neural Sentence Ordering

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Enhancing AMR-to-Text Generation with Dual Graph Representations

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Lattice CNNs for Matching Based Chinese Question Answering

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Heterogeneous Graph Transformer for Graph-to-Sequence Learning

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Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs

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Sentence-Level Content Planning and Style Specification for Neural Text Generation

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Enhancing Scientific Papers Summarization with Citation Graph

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Describing a Knowledge Base

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Coherent Comments Generation for Chinese Articles with a Graph-to-Sequence Model

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Semantic Representation for Dialogue Modeling

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Text Generation from Knowledge Graphs with Graph TransformersPreprint

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Multi-hop Question Generation with Graph Convolutional Network

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FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations

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Modeling Intent Graph for Search Result Diversification

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Relation Extraction Exploiting Full Dependency Forests

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Knowledge graph based natural language generation with adapted pointer-generator networks

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Gated Graph Neural Attention Networks for abstractive summarization

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Enhancing Abstractive Summarization with Extracted Knowledge Graphs and Multi-Source Transformers

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AMR Similarity Metrics from Principles

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Logic-Consistency Text Generation from Semantic Parses

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Graph Neural Networks for Natural Language Processing: A Survey

Wu, L., Chen, Y., Shen, K., Guo, X., Gao, H., Li, S., Pei, J., & Long, B. (2023) · doi:10.1561/9781638281436 · Cited by 26 · see citing works

Neural Topic Modeling by Incorporating Document Relationship Graph

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Hierarchical Neighbor Propagation With Bidirectional Graph Attention Network for Relation Prediction

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Decoding Methods in Neural Language Generation: A Survey

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A Syntax-Augmented and Headline-Aware Neural Text Summarization Method

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Does Pretraining for Summarization Require Knowledge Transfer?

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Employing Argumentation Knowledge Graphs for Neural Argument Generation

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A Survey on Neural Data-to-Text Generation

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Summarizing Chinese Medical Answer with Graph Convolution Networks and Question-focused Dual Attention

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A Tree-to-Sequence Model for Neural NLG in Task-Oriented Dialog

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Online Back-Parsing for AMR-to-Text Generation

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Unifying Structured Data as Graph for Data-to-Text Pre-Training

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GraphTTS: Graph-to-Sequence Modelling in Neural Text-to-Speech

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RDF-to-Text Generation with Graph-augmented Structural Neural Encoders

Gao, H., Wu, L., Hu, P., & Xu, F. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/419 · Cited by 16 · see citing works

Cross-sentence N-ary relation classification using LSTMs on graph and sequence structures

Zhao, L., Xu, W., Gao, S., & Guo, J. H. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106266 · Cited by 16 · see citing works

Multi-modal graph contrastive encoding for neural machine translation

Yin, Y., Zeng, J., Su, J., Zhou, C., Meng, F., Zhou, J., Huang, D., & Luo, J. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2023.103986 · Cited by 15 · see citing works

Deep learning for spatio‐temporal supply and demand forecasting in natural gas transmission networks

Petković, M., Koch, T., & Zittel, J. (2021) · Energy Science & Engineering · doi:10.1002/ese3.932 · Cited by 15 · see citing works

End-to-End AMR Corefencence Resolution

Fu, Q., Song, L., Du, W., & Zhang, Y. (2021) · doi:10.18653/v1/2021.acl-long.324 · Cited by 14 · see citing works

Question answering with deep neural networks for semi-structured heterogeneous genealogical knowledge graphs

Suissa, O., Zhitomirsky‐Geffet, M., & Elmalech, A. (2022) · Semantic Web · doi:10.3233/sw-222925 · Cited by 14 · see citing works

Evidence Integration for Multi-Hop Reading Comprehension With Graph Neural Networks

Song, L., Wang, Z., Yu, M., Zhang, Y., Florian, R., & Gildea, D. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2982894 · Cited by 13 · see citing works

Stage-wise Fine-tuning for Graph-to-Text Generation

Wang, Q., Yavuz, S., Lin, X., Ji, H., & Rajani, N. F. (2021) · doi:10.18653/v1/2021.acl-srw.2 · Cited by 14 · see citing works

A Human Evaluation of AMR-to-English Generation Systems

Manning, E., Wein, S., & Schneider, N. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.420 · Cited by 12 · see citing works

Recent advances of neural text generation: Core tasks, datasets, models and challenges

Jin, H., Cao, Y., Wang, T., Xing, X., & Wan, X. (2020) · Science China Technological Sciences · doi:10.1007/s11431-020-1622-y · Cited by 12 · see citing works

Extractive text-image summarization with relation-enhanced graph attention network

Xie, F., Chen, J., & Chen, K. (2022) · Journal of Intelligent Information Systems · doi:10.1007/s10844-022-00757-x · Cited by 13 · see citing works

A divide-and-conquer approach to neural natural language generation from structured data

Dethlefs, N., Schoene, A. M., & Cuayáhuitl, H. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.12.083 · Cited by 12 · see citing works

XLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation

Xu, D., Li, J., Zhu, M., Zhang, M., & Zhou, G. (2021) · doi:10.18653/v1/2021.acl-long.73 · Cited by 11 · see citing works

Variational Graph Autoencoding as Cheap Supervision for AMR Coreference Resolution

Li, I., Song, L., Xu, K., & Yu, D. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.199 · Cited by 11 · see citing works

Evaluating Generative Models for Graph-to-Text Generation

Yuan, S., & Färber, M. (2023) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-092-2_133 · Cited by 15 · see citing works

A Deep Learning Algorithms to Generate Activity Sequences Using Historical As-built Schedule Data

Hamed, A., Chau, L., & David, J. H. (2020) · Creative Construction e-Conference · doi:10.3311/ccc2020-039 · Cited by 11 · see citing works

Utilizing graph neural networks to improving dialogue-based relation extraction

Zhao, L., Xu, W., Gao, S., & Guo, J. H. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.05.082 · Cited by 11 · see citing works

Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligence

Balhorn, L. S., Degens, K., & Schweidtmann, A. M. (2025) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2025.109121 · Cited by 12 · see citing works

Peking at MRP 2019: Factorization- and Composition-Based Parsing for Elementary Dependency Structures

Chen, Y., Ye, Y., & Sun, W. (2019) · doi:10.18653/v1/k19-2016 · Cited by 9 · see citing works

Modeling Global and Local Interactions for Online Conversation Recommendation

Zeng, X., Li, J., Wang, L., & Wong, K. (2021) · ACM Transactions on Information Systems · doi:10.1145/3473970 · Cited by 10 · see citing works

Graph Neural Networks in Data Mining

Ma, Y., & Tang, J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108924184.017 · Cited by 10 · see citing works

Factorising AMR generation through syntaxPreprint

Cao, K., & Clark, S. C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.07707 · Cited by 10 · see citing works

Recursive Graphical Neural Networks for Text ClassificationPreprint

Li, W., Li, S., Ma, S., He, Y., Chen, D., & Sun, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.08166 · Cited by 10 · see citing works

Improving Language Generation from Feature-Rich Tree-Structured Data with Relational Graph Convolutional Encoders

Hong, X., Chang, E., & Demberg, V. (2019) · Workshop on Multilingual Surface Realisation (MSR) · doi:10.18653/v1/d19-6310 · Cited by 9 · see citing works

Generating Natural Language From Logic Expressions With Structural Representation

Wu, X., Cai, Y., Lian, Z., Leung, H.-F., & Wang, T. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3263784 · Cited by 9 · see citing works

Structural Neural Encoders for AMR-to-text GenerationPreprint

Damonte, M., & Cohen, S. B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.11410 · Cited by 9 · see citing works

Graph Neural Networks in Natural Language Processing

Ma, Y., & Tang, J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108924184.015 · Cited by 8 · see citing works

DRTS Parsing with Structure-Aware Encoding and Decoding

Fu, Q., Zhang, Y., Liu, J., & Zhang, M. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.609 · Cited by 8 · see citing works

GraphPB: Graphical Representations of Prosody Boundary in Speech Synthesis

Sun, A., Wang, J., Cheng, N., Peng, H., Zeng, Z., Kong, L., & Xiao, J. Z. (2021) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt48900.2021.9383530 · Cited by 8 · see citing works

Abstract meaning representation of Turkish

Oral, E., Acar, A., & Eryiğit, G. (2022) · Natural Language Engineering · doi:10.1017/s1351324922000183 · Cited by 8 · see citing works

Keywords-aware dynamic graph neural network for multi-hop reading comprehension

Jia, M., Liao, L., Wang, W., Li, F., Chen, Z., Li, J., & Huang, H. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.05.110 · Cited by 7 · see citing works

Generating Text from Anonymised Structures

Colin, É., & Gardent, C. (2019) · International Conference on Natural Language Generation · doi:10.18653/v1/w19-8614 · Cited by 7 · see citing works

Coarse and Fine Granularity Graph Reasoning for Interpretable Multi-Hop Question Answering

Zhang, M., Li, F., Wang, Y., Zhang, Z., Zhou, Y., & Li, X. (2020) · IEEE Access · doi:10.1109/access.2020.2981134 · Cited by 7 · see citing works

Evaluating Text Generation from Discourse Representation Structures

Wang, C., van Noord, R., Bisazza, A., & Bos, J. (2021) · Workshop on Generation, Evaluation and Metrics (GEM) · doi:10.18653/v1/2021.gem-1.8 · Cited by 7 · see citing works

Better AMR-To-Text Generation with Graph Structure Reconstruction

Wang, T., Wan, X., & Yao, S. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/542 · Cited by 20 · see citing works

AMR-To-Text Generation with Graph Transformer

Wang, T., Wan, X., & Jin, H. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00297 · Cited by 65 · see citing works

Structural Neural Encoders for AMR-to-text GenerationPreprint

Damonte, M., & Cohen, S. B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.11410 · Cited by 9 · see citing works

Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks

Zhao, Y., Chen, L., Chen, Z., Cao, R., Zhu, S., & Yu, K. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.67 · Cited by 34 · see citing works

Enhancing AMR-to-Text Generation with Dual Graph Representations

Ribeiro, L. F. R., Gardent, C., & Gurevych, I. (2019) · TUbilio (Technical University of Darmstadt) · doi:10.48550/arxiv.1909.00352 · Cited by 3 · see citing works