# End-to-End Content and Plan Selection for Data-to-Text Generation

arXiv: https://arxiv.org/abs/1810.04700 · HTML: https://mlsanity.com/paper/1810.04700/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2018.

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

- [HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation](https://mlsanity.com/paper/2108.06712/) (2021)
- [Plan-then-Generate: Controlled Data-to-Text Generation via Planning](https://mlsanity.com/paper/2108.13740/) (2021)
- [Aspect-Controllable Opinion Summarization](https://mlsanity.com/paper/2109.03171/) (2021)
- [Controlling Hallucinations at Word Level in Data-to-Text Generation](https://mlsanity.com/paper/2102.02810/) (2021)
- [Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning](https://mlsanity.com/paper/2205.03972/) (2022)
- [Few-Shot Table-to-Text Generation with Prototype Memory](https://mlsanity.com/paper/2108.12516/) (2021)
- [Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges](https://mlsanity.com/paper/2405.15604/) (2024)
- [LitLLMs, LLMs for Literature Review: Are we there yet?](https://mlsanity.com/paper/2412.15249/) (2024)
- [Innovations in Neural Data-to-text Generation: A Survey](https://mlsanity.com/paper/2207.12571/) (2022)
- [TCube: Domain-Agnostic Neural Time-series Narration](https://mlsanity.com/paper/2110.05633/) (2021)
- [Text Style Transfer: An Introductory Overview](https://mlsanity.com/paper/2407.14822/) (2024)
- [A Systematic Review of Data-to-Text NLG](https://mlsanity.com/paper/2402.08496/) (2024)
- [May the Force Be with Your Copy Mechanism: Enhanced Supervised-Copy Method for Natural Language Generation](https://mlsanity.com/paper/2112.10360/) (2021)
- [SPOR: A Comprehensive and Practical Evaluation Method for Compositional Generalization in Data-to-Text Generation](https://mlsanity.com/paper/2405.10650/) (2024)
- [Curriculum-Based Self-Training Makes Better Few-Shot Learners for Data-to-Text Generation](https://mlsanity.com/paper/2206.02712/) (2022)
- [Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning](https://mlsanity.com/paper/2308.05317/) (2023)
- [Transforming Multi-Conditioned Generation from Meaning Representation](https://mlsanity.com/paper/2101.04257/) (2021)
- [Generalising Multilingual Concept-to-Text NLG with Language Agnostic Delexicalisation](https://mlsanity.com/paper/2105.03432/) (2021)
- [Development of REGAI: Rubric Enabled Generative Artificial Intelligence](https://mlsanity.com/paper/2408.02811/) (2024)
- [Informative Text Generation from Knowledge Triples](https://mlsanity.com/paper/2209.12733/) (2022)

## Abstract (arXiv)

Learning to generate fluent natural language from structured data with neural networks has become an common approach for NLG. This problem can be challenging when the form of the structured data varies between examples. This paper presents a survey of several extensions to sequence-to-sequence models to account for the latent content selection process, particularly variants of copy attention and coverage decoding. We further propose a training method based on diverse ensembling to encourage models to learn distinct sentence templates during training. An empirical evaluation of these techniques shows an increase in the quality of generated text across five automated metrics, as well as human evaluation.