# Simple-QE: Better Automatic Quality Estimation for Text Simplification

arXiv: https://arxiv.org/abs/2012.12382 · HTML: https://mlsanity.com/paper/2012.12382/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [On the Limitations of Reference-Free Evaluations of Generated Text](https://mlsanity.com/paper/2210.12563/) (2022)
- [Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA](https://mlsanity.com/paper/2305.14458/) (2023)
- [REFeREE: A REference-FREE Model-Based Metric for Text Simplification](https://mlsanity.com/paper/2403.17640/) (2024)
- [Label Confidence Weighted Learning for Target-level Sentence Simplification](https://mlsanity.com/paper/2410.05748/) (2024)
- [Automatic Text Simplification of News Articles in the Context of Public Broadcasting](https://mlsanity.com/paper/2212.13317/) (2022)

## Abstract (arXiv)

Text simplification systems generate versions of texts that are easier to understand for a broader audience. The quality of simplified texts is generally estimated using metrics that compare to human references, which can be difficult to obtain. We propose Simple-QE, a BERT-based quality estimation (QE) model adapted from prior summarization QE work, and show that it correlates well with human quality judgments. Simple-QE does not require human references, which makes the model useful in a practical setting where users would need to be informed about the quality of generated simplifications. We also show that we can adapt this approach to accurately predict the complexity of human-written texts.