# Readability-based Sentence Ranking for Evaluating Text Simplification

arXiv: https://arxiv.org/abs/1603.06009 · HTML: https://mlsanity.com/paper/1603.06009/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2016.

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

- [Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features](https://mlsanity.com/paper/2109.12258/) (2021)
- [BERT Embeddings for Automatic Readability Assessment](https://mlsanity.com/paper/2106.07935/) (2021)
- [Uniform Complexity for Text Generation](https://mlsanity.com/paper/2204.05185/) (2022)
- [Zero-Shot Grammar Competency Estimation Using Large Language Model Generated Pseudo Labels](https://mlsanity.com/paper/2511.13152/) (2025)
- [Translate or Simplify First: An Analysis of Cross-lingual Text Simplification in English and French](https://mlsanity.com/paper/2604.23844/) (2026)

## Abstract (arXiv)

We propose a new method for evaluating the readability of simplified sentences through pair-wise ranking. The validity of the method is established through in-corpus and cross-corpus evaluation experiments. The approach correctly identifies the ranking of simplified and unsimplified sentences in terms of their reading level with an accuracy of over 80%, significantly outperforming previous results. To gain qualitative insights into the nature of simplification at the sentence level, we studied the impact of specific linguistic features. We empirically confirm that both word-level and syntactic features play a role in comparing the degree of simplification of authentic data. To carry out this research, we created a new sentence-aligned corpus from professionally simplified news articles. The new corpus resource enriches the empirical basis of sentence-level simplification research, which so far relied on a single resource. Most importantly, it facilitates cross-corpus evaluation for simplification, a key step towards generalizable results.