mlsanityData to 14 Oct 2026

Survey of Abstract Meaning Representation: Then, Now, Future

arXiv 2505.03229 ↗First posted 6 May 2025cs.CLcited by 2

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Abstract (arXiv)

This paper presents a survey of Abstract Meaning Representation (AMR), a semantic representation framework that captures the meaning of sentences through a graph-based structure. AMR represents sentences as rooted, directed acyclic graphs, where nodes correspond to concepts and edges denote relationships, effectively encoding the meaning of complex sentences. This survey investigates AMR and its extensions, focusing on AMR capabilities. It then explores the parsing (text-to-AMR) and generation (AMR-to-text) tasks by showing traditional, current, and possible futures approaches. It also reviews various applications of AMR including text generation, text classification, and information extraction and information seeking. By analyzing recent developments and challenges in the field, this survey provides insights into future directions for research and the potential impact of AMR on enhancing machine understanding of human language.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 46 of its 112 references that are in the map. First posted on arXiv 6 May 2025.

Builds on

46 of 112 references.

References · 46

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  3. 2020Multilingual Translation with Extensible Multilingual Pretraining and Finetuningcited by 287
  4. 2015DenseCap: Fully Convolutional Localization Networks for Dense Captioningcited by 176
  5. 2022MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languagescited by 146
  6. 2017Neural AMR: Sequence-to-Sequence Models for Parsing and Generationcited by 56
  7. 2018A Graph-to-Sequence Model for AMR-to-Text Generationcited by 56
  8. 2022Graph Pre-training for AMR Parsing and Generationcited by 55
  9. 2019AMR Parsing as Sequence-to-Graph Transductioncited by 50
  10. 2021Structural Adapters in Pretrained Language Models for AMR-to-text Generationcited by 48
  11. 2018Toward Abstractive Summarization Using Semantic Representationscited by 43
  12. 2020GPT-too: A language-model-first approach for AMR-to-text generationcited by 38
34 more
  1. 2019Semantic Neural Machine Translation using AMRcited by 37
  2. 2018AMR Parsing as Graph Prediction with Latent Alignmentcited by 36
  3. 2018Abstract Meaning Representation for Multi-Document Summarizationcited by 35
  4. 2019Modeling Graph Structure in Transformer for Better AMR-to-Text Generationcited by 35
  5. 2016An Incremental Parser for Abstract Meaning Representationcited by 34
  6. 2021AMR Parsing with Action-Pointer Transformercited by 25
  7. 2021Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsingcited by 25
  8. 2018Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representationcited by 24
  9. 2021Semantic Representation for Dialogue Modelingcited by 24
  10. 2020AMR Similarity Metrics from Principlescited by 22
  11. 2023SMATCH++: Standardized and Extended Evaluation of Semantic Graphscited by 21
  12. 2019Structural Neural Encoders for AMR-to-text Generationcited by 19
  13. 2021Weisfeiler-Leman in the BAMBOO: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similaritycited by 16
  14. 2022A Two-Stream AMR-enhanced Model for Document-level Event Argument Extractioncited by 16
  15. 2020Promoting Graph Awareness in Linearized Graph-to-Text Generationcited by 14
  16. 2019SemBleu: A Robust Metric for AMR Parsing Evaluationcited by 12
  17. 2020Multilingual AMR-to-Text Generationcited by 12
  18. 2021DocAMR: Multi-Sentence AMR Representation and Evaluationcited by 12
  19. 2022Interpretable AMR-Based Question Decomposition for Multi-hop Question Answeringcited by 12
  20. 2017Addressing the Data Sparsity Issue in Neural AMR Parsingcited by 11
  21. 2023ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translationcited by 11
  22. 2023Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representationcited by 10
  23. 2021Probabilistic, Structure-Aware Algorithms for Improved Variety, Accuracy, and Coverage of AMR Alignmentscited by 9
  24. 2021Multilingual AMR Parsing with Noisy Knowledge Distillationcited by 8
  25. 2024ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptionscited by 8
  26. 2023Incorporating Graph Information in Transformer-based AMR Parsingcited by 7
  27. 2023AMR Parsing is Far from Solved: GrAPES, the Granular AMR Parsing Evaluation Suitecited by 6
  28. 2017AMR-to-text Generation with Synchronous Node Replacement Grammarcited by 5
  29. 2019SEMA: an Extended Semantic Evaluation Metric for AMRcited by 4
  30. 2022Visual Semantic Parsing: From Images to Abstract Meaning Representationcited by 4
  31. 2024MASSIVE Multilingual Abstract Meaning Representation: A Dataset and Baselines for Hallucination Detectioncited by 4
  32. 2024AMREx: AMR for Explainable Fact Verificationcited by 4
  33. 2024DART: An AIGT Detector using AMR of Rephrased Textcited by 4
  34. 2023Investigating the Effect of Relative Positional Embeddings on AMR-to-Text Generation with Structural Adapterscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025SAFT: Structure-Aware Fine-Tuning of LLMs for AMR-to-Text Generationcited by 0
  2. 2026Ontology for Policing: Conceptual Knowledge Learning for Semantic Understanding and Reasoning in Law Enforcement Reportscited by 0