# Evaluating NLG systems: A brief introduction

arXiv: https://arxiv.org/abs/2303.16742 · HTML: https://mlsanity.com/paper/2303.16742/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 10 references that are in the map.
- First posted on arXiv 29 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [Evaluation of Text Generation: A Survey](https://mlsanity.com/paper/2006.14799/) (2020)
- [A Survey of Evaluation Metrics Used for NLG Systems](https://mlsanity.com/paper/2008.12009/) (2020)
- [Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text](https://mlsanity.com/paper/2202.06935/) (2022)
- [Preregistering NLP Research](https://mlsanity.com/paper/2103.06944/) (2021)
- [The Human Evaluation Datasheet 1.0: A Template for Recording Details of Human Evaluation Experiments in NLP](https://mlsanity.com/paper/2103.09710/) (2021)
- [Underreporting of errors in NLG output, and what to do about it](https://mlsanity.com/paper/2108.01182/) (2021)

## Abstract (arXiv)

This year the International Conference on Natural Language Generation (INLG) will feature an award for the paper with the best evaluation. The purpose of this award is to provide an incentive for NLG researchers to pay more attention to the way they assess the output of their systems. This essay provides a short introduction to evaluation in NLG, explaining key terms and distinctions.