# Traditional Readability Formulas Compared for English

arXiv: https://arxiv.org/abs/2301.02975 · HTML: https://mlsanity.com/paper/2301.02975/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 69 references that are in the map.
- First posted on arXiv 8 Jan 2023.

## Builds on (most cited first, 10 of 10)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations](https://mlsanity.com/paper/2005.00481/) (2020)
- [Text Readability Assessment for Second Language Learners](https://mlsanity.com/paper/1906.07580/) (2019)
- [Fast and Accurate Neural CRF Constituency Parsing](https://mlsanity.com/paper/2008.03736/) (2020)
- [Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features](https://mlsanity.com/paper/2109.12258/) (2021)
- [ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysis](https://mlsanity.com/paper/2103.04083/) (2021)
- [LXPER Index 2.0: Improving Text Readability Assessment Model for L2 English Students in Korea](https://mlsanity.com/paper/2010.13374/) (2020)
- [LXPER Index: a curriculum-specific text readability assessment model for EFL students in Korea](https://mlsanity.com/paper/2008.01564/) (2020)

## Built on by (most cited first, 4 of 4)

- [LFTK: Handcrafted Features in Computational Linguistics](https://mlsanity.com/paper/2305.15878/) (2023)
- [Prompt-based Learning for Text Readability Assessment](https://mlsanity.com/paper/2302.13139/) (2023)
- [Do LLMs Implicitly Determine the Suitable Text Difficulty for Users?](https://mlsanity.com/paper/2402.14453/) (2024)
- [Exploring Linguistic Features for Turkish Text Readability](https://mlsanity.com/paper/2306.03774/) (2023)

## Abstract (arXiv)

Traditional English readability formulas, or equations, were largely developed in the 20th century. Nonetheless, many researchers still rely on them for various NLP applications. This phenomenon is presumably due to the convenience and straightforwardness of readability formulas. In this work, we contribute to the NLP community by 1. introducing New English Readability Formula (NERF), 2. recalibrating the coefficients of old readability formulas (Flesch-Kincaid Grade Level, Fog Index, SMOG Index, Coleman-Liau Index, and Automated Readability Index), 3. evaluating the readability formulas, for use in text simplification studies and medical texts, and 4. developing a Python-based program for the wide application to various NLP projects.