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A Hierarchical Model for Data-to-Text Generation

arXiv 1912.10011 ↗First posted 20 Dec 2019cs.CL cs.IR cs.LGcited by 20

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Abstract (arXiv)

Transcribing structured data into natural language descriptions has emerged as a challenging task, referred to as "data-to-text". These structures generally regroup multiple elements, as well as their attributes. Most attempts rely on translation encoder-decoder methods which linearize elements into a sequence. This however loses most of the structure contained in the data. In this work, we propose to overpass this limitation with a hierarchical model that encodes the data-structure at the element-level and the structure level. Evaluations on RotoWire show the effectiveness of our model w.r.t. qualitative and quantitative metrics.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Dec 2019.

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Citing papers per year: 2021: 6; 2022: 3; 2023: 4; 2024: 5; 2025: 0; 2026: 2.

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  1. 2021Controlling Hallucinations at Word Level in Data-to-Text Generationcited by 41
  2. 2021Data-to-text Generation with Macro Planningcited by 34
  3. 2023Long Text and Multi-Table Summarization: Dataset and Methodcited by 23
  4. 2022Data-to-text Generation with Variational Sequential Planningcited by 17
  5. 2024Beyond Traditional Benchmarks: Analyzing Behaviors of Open LLMs on Data-to-Text Generationcited by 16
  6. 2024Unifying Structured Data as Graph for Data-to-Text Pre-Trainingcited by 13
  7. 2022Innovations in Neural Data-to-text Generation: A Surveycited by 10
  8. 2021Generation Challenges: Results of the Accuracy Evaluation Shared Taskcited by 9
  9. 2021TCube: Domain-Agnostic Neural Time-series Narrationcited by 5
  10. 2023Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text Generationcited by 5
  11. 2021May the Force Be with Your Copy Mechanism: Enhanced Supervised-Copy Method for Natural Language Generationcited by 3
  12. 2022Commentary Generation from Data Records of Multiplayer Strategy Esports Gamecited by 3
8 more
  1. 2024Improving Factual Accuracy of Neural Table-to-Text Output by Addressing Input Problems in ToTTocited by 3
  2. 2023You Can Generate It Again: Data-to-Text Generation with Verification and Correction Promptingcited by 2
  3. 2023Automatic Logical Forms improve fidelity in Table-to-Text generationcited by 2
  4. 2021Table Caption Generation in Scholarly Documents Leveraging Pre-trained Language Modelscited by 1
  5. 2024Facts-and-Feelings: Capturing both Objectivity and Subjectivity in Table-to-Text Generationcited by 1
  6. 2024Self-training from Self-memory in Data-to-text Generationcited by 0
  7. 2026EpiNarrate: Agentic Generation of Grounded Narratives from Epidemiological Scenario Projectionscited by 0
  8. 2026Cross-Domain, Multi-Task Data-to-Text Generation without In-Domain Training Datacited by 0