mlsanityData to 7 Oct 2026

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

arXiv 1805.02473 ↗First posted 7 May 2018cs.CLcited by 56

Not on the map. Why?

Abstract (arXiv)

The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph. The current state-of-the-art method uses a sequence-to-sequence model, leveraging LSTM for encoding a linearized AMR structure. Although being able to model non-local semantic information, a sequence LSTM can lose information from the AMR graph structure, and thus faces challenges with large graphs, which result in long sequences. We introduce a neural graph-to-sequence model, using a novel LSTM structure for directly encoding graph-level semantics. On a standard benchmark, our model shows superior results to existing methods in the literature.

Key facts. Cited by 56 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 May 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 16; 2022: 15; 2023: 11; 2024: 7; 2025: 6; 2026: 1.

56 papers.

Sort:

By area:

  1. 2023A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPTcited by 221
  2. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 170
  3. 2022Text Generation with Diffusion Language Models: A Pre-training Approach with Continuous Paragraph Denoisecited by 79
  4. 2022Graph Pre-training for AMR Parsing and Generationcited by 55
  5. 2021Structural Adapters in Pretrained Language Models for AMR-to-text Generationcited by 48
  6. 2022FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representationscited by 41
  7. 2023Towards Understanding the Generalization of Graph Neural Networkscited by 41
  8. 2021Does Pretraining for Summarization Require Knowledge Transfer?cited by 30
  9. 2022Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methodscited by 27
  10. 2021Structural Information Preserving for Graph-to-Text Generationcited by 24
  11. 2021Semantic Representation for Dialogue Modelingcited by 24
  12. 2021Logic-Consistency Text Generation from Semantic Parsescited by 23
38 more
  1. 2023Evaluating Generative Models for Graph-to-Text Generationcited by 16
  2. 2021Enhancing Scientific Papers Summarization with Citation Graphcited by 14
  3. 2024Unifying Structured Data as Graph for Data-to-Text Pre-Trainingcited by 13
  4. 2021Stage-wise Fine-tuning for Graph-to-Text Generationcited by 11
  5. 2023ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translationcited by 11
  6. 2021Constrained Text Generation with Global Guidance -- Case Study on CommonGencited by 10
  7. 2021Graphine: A Dataset for Graph-aware Terminology Definition Generationcited by 10
  8. 2022Generic and Trend-aware Curriculum Learning for Relation Extraction in Graph Neural Networkscited by 9
  9. 2023LLM Based Multi-Document Summarization Exploiting Main-Event Biased Monotone Submodular Content Extractioncited by 7
  10. 2022T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representationcited by 6
  11. 2021LOGEN: Few-shot Logical Knowledge-Conditioned Text Generation with Self-trainingcited by 5
  12. 2022A Dynamic, Interpreted CheckList for Meaning-oriented NLG Metric Evaluation -- through the Lens of Semantic Similarity Ratingcited by 5
  13. 2024Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligencecited by 5
  14. 2022Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representationcited by 4
  15. 2024A Systematic Review of Data-to-Text NLGcited by 4
  16. 2026TARSE: Test-Time Adaptation via Retrieval of Skills and Experience for Reasoning Agentscited by 4
  17. 2021Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generationcited by 3
  18. 2022Semantic-based Pre-training for Dialogue Understandingcited by 3
  19. 2023CATS: A Pragmatic Chinese Answer-to-Sequence Dataset with Large Scale and High Qualitycited by 3
  20. 2025SR-LLM: Rethinking the Structured Representation in Large Language Modelcited by 3
  21. 2021Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency Graphcited by 2
  22. 2022Graph-to-Text Generation with Dynamic Structure Pruningcited by 2
  23. 2022Scene Graph Modification as Incremental Structure Expandingcited by 2
  24. 2022Cross-domain Generalization for AMR Parsingcited by 2
  25. 2023Investigating the Effect of Relative Positional Embeddings on AMR-to-Text Generation with Structural Adapterscited by 2
  26. 2023Question Answering with Deep Neural Networks for Semi-Structured Heterogeneous Genealogical Knowledge Graphscited by 2
  27. 2023Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learningcited by 2
  28. 2024GraSAME: Injecting Token-Level Structural Information to Pretrained Language Models via Graph-guided Self-Attention Mechanismcited by 2
  29. 2024Sequence Graph Network for Online Debate Analysiscited by 2
  30. 2025Survey of Abstract Meaning Representation: Then, Now, Futurecited by 2
  31. 2021Triples-to-Text Generation with Reinforcement Learning Based Graph-augmented Neural Networkscited by 1
  32. 2022Pre-Training a Graph Recurrent Network for Language Representationcited by 1
  33. 2022Neural Controller Synthesis for Signal Temporal Logic Specifications Using Encoder-Decoder Structured Networkscited by 1
  34. 2023Graph Attention with Hierarchies for Multi-hop Question Answeringcited by 1
  35. 2023Reranking for Natural Language Generation from Logical Forms: A Study based on Large Language Modelscited by 1
  36. 2021Graph Neural Networks for Natural Language Processing: A Surveycited by 0
  37. 2021Semantic-Based Self-Critical Training For Question Generationcited by 0
  38. 2021Tree Decomposition Attention for AMR-to-Text Generationcited by 0

50 most cited of 56 All 56 (JSON)