# A Hierarchical Model for Data-to-Text Generation

arXiv: https://arxiv.org/abs/1912.10011 · HTML: https://mlsanity.com/paper/1912.10011/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2019.

## Built on by (most cited first, 20 of 20)

- [Controlling Hallucinations at Word Level in Data-to-Text Generation](https://mlsanity.com/paper/2102.02810/) (2021)
- [Data-to-text Generation with Macro Planning](https://mlsanity.com/paper/2102.02723/) (2021)
- [Long Text and Multi-Table Summarization: Dataset and Method](https://mlsanity.com/paper/2302.03815/) (2023)
- [Data-to-text Generation with Variational Sequential Planning](https://mlsanity.com/paper/2202.13756/) (2022)
- [Beyond Traditional Benchmarks: Analyzing Behaviors of Open LLMs on Data-to-Text Generation](https://mlsanity.com/paper/2401.10186/) (2024)
- [Unifying Structured Data as Graph for Data-to-Text Pre-Training](https://mlsanity.com/paper/2401.01183/) (2024)
- [Innovations in Neural Data-to-text Generation: A Survey](https://mlsanity.com/paper/2207.12571/) (2022)
- [Generation Challenges: Results of the Accuracy Evaluation Shared Task](https://mlsanity.com/paper/2108.05644/) (2021)
- [TCube: Domain-Agnostic Neural Time-series Narration](https://mlsanity.com/paper/2110.05633/) (2021)
- [Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text Generation](https://mlsanity.com/paper/2311.09467/) (2023)
- [May the Force Be with Your Copy Mechanism: Enhanced Supervised-Copy Method for Natural Language Generation](https://mlsanity.com/paper/2112.10360/) (2021)
- [Commentary Generation from Data Records of Multiplayer Strategy Esports Game](https://mlsanity.com/paper/2212.10935/) (2022)
- [Improving Factual Accuracy of Neural Table-to-Text Output by Addressing Input Problems in ToTTo](https://mlsanity.com/paper/2404.04103/) (2024)
- [You Can Generate It Again: Data-to-Text Generation with Verification and Correction Prompting](https://mlsanity.com/paper/2306.15933/) (2023)
- [Automatic Logical Forms improve fidelity in Table-to-Text generation](https://mlsanity.com/paper/2310.17279/) (2023)
- [Table Caption Generation in Scholarly Documents Leveraging Pre-trained Language Models](https://mlsanity.com/paper/2108.08111/) (2021)
- [Facts-and-Feelings: Capturing both Objectivity and Subjectivity in Table-to-Text Generation](https://mlsanity.com/paper/2406.10560/) (2024)
- [EpiNarrate: Agentic Generation of Grounded Narratives from Epidemiological Scenario Projections](https://mlsanity.com/paper/2607.15544/) (2026)
- [Self-training from Self-memory in Data-to-text Generation](https://mlsanity.com/paper/2401.10567/) (2024)
- [Cross-Domain, Multi-Task Data-to-Text Generation without In-Domain Training Data](https://mlsanity.com/paper/2608.23391/) (2026)

## 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.