# Supervised and Unsupervised Neural Approaches to Text Readability

arXiv: https://arxiv.org/abs/1907.11779 · HTML: https://mlsanity.com/paper/1907.11779/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2019.

## Built on by (most cited first, 30 of 46)

- [Imprompter: Tricking LLM Agents into Improper Tool Use](https://mlsanity.com/paper/2410.14923/) (2024)
- [Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features](https://mlsanity.com/paper/2109.12258/) (2021)
- [Trends, Limitations and Open Challenges in Automatic Readability Assessment Research](https://mlsanity.com/paper/2105.00973/) (2021)
- [CEFR-Based Sentence Difficulty Annotation and Assessment](https://mlsanity.com/paper/2210.11766/) (2022)
- [Generating Summaries with Controllable Readability Levels](https://mlsanity.com/paper/2310.10623/) (2023)
- [BERT Embeddings for Automatic Readability Assessment](https://mlsanity.com/paper/2106.07935/) (2021)
- [Readability Controllable Biomedical Document Summarization](https://mlsanity.com/paper/2210.04705/) (2022)
- [ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment](https://mlsanity.com/paper/2305.14463/) (2023)
- [Generating and Evaluating Tests for K-12 Students with Language Model Simulations: A Case Study on Sentence Reading Efficiency](https://mlsanity.com/paper/2310.06837/) (2023)
- [A Neural Pairwise Ranking Model for Readability Assessment](https://mlsanity.com/paper/2203.07450/) (2022)
- [Personalized Jargon Identification for Enhanced Interdisciplinary Communication](https://mlsanity.com/paper/2311.09481/) (2023)
- [Measuring and Modifying the Readability of English Texts with GPT-4](https://mlsanity.com/paper/2410.14028/) (2024)
- [Prompt-based Learning for Text Readability Assessment](https://mlsanity.com/paper/2302.13139/) (2023)
- [Automatic Readability Assessment of German Sentences with Transformer Ensembles](https://mlsanity.com/paper/2209.04299/) (2022)
- [A Unified Neural Network Model for Readability Assessment with Feature Projection and Length-Balanced Loss](https://mlsanity.com/paper/2210.10305/) (2022)
- [TriviaHG: A Dataset for Automatic Hint Generation from Factoid Questions](https://mlsanity.com/paper/2403.18426/) (2024)
- [An Open Multilingual System for Scoring Readability of Wikipedia](https://mlsanity.com/paper/2406.01835/) (2024)
- [Engagement-Driven Content Generation with Large Language Models](https://mlsanity.com/paper/2411.13187/) (2024)
- [Predicting Text Readability from Scrolling Interactions](https://mlsanity.com/paper/2105.06354/) (2021)
- [Ace-CEFR -- A Dataset for Automated Evaluation of the Linguistic Difficulty of Conversational Texts for LLM Applications](https://mlsanity.com/paper/2506.14046/) (2025)
- [Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment](https://mlsanity.com/paper/2107.04268/) (2021)
- [Label Confidence Weighted Learning for Target-level Sentence Simplification](https://mlsanity.com/paper/2410.05748/) (2024)
- [Zero-shot Large Language Models for Automatic Readability Assessment](https://mlsanity.com/paper/2604.24470/) (2026)
- [A Transfer Learning Based Model for Text Readability Assessment in German](https://mlsanity.com/paper/2207.06265/) (2022)
- [Strategies for Arabic Readability Modeling](https://mlsanity.com/paper/2407.03032/) (2024)
- [A Simple Post-Processing Technique for Improving Readability Assessment of Texts using Word Mover's Distance](https://mlsanity.com/paper/2103.07277/) (2021)
- [Semi-Supervised Joint Estimation of Word and Document Readability](https://mlsanity.com/paper/2104.13103/) (2021)
- [Pseudo-Labels Are All You Need](https://mlsanity.com/paper/2208.09243/) (2022)
- [Sentence Ambiguity, Grammaticality and Complexity Probes](https://mlsanity.com/paper/2210.06928/) (2022)
- [A User-Centered Evaluation of Spanish Text Simplification](https://mlsanity.com/paper/2308.07556/) (2023)

## Abstract (arXiv)

We present a set of novel neural supervised and unsupervised approaches for determining the readability of documents. In the unsupervised setting, we leverage neural language models, whereas in the supervised setting, three different neural classification architectures are tested. We show that the proposed neural unsupervised approach is robust, transferable across languages and allows adaptation to a specific readability task and data set. By systematic comparison of several neural architectures on a number of benchmark and new labelled readability datasets in two languages, this study also offers a comprehensive analysis of different neural approaches to readability classification. We expose their strengths and weaknesses, compare their performance to current state-of-the-art classification approaches to readability, which in most cases still rely on extensive feature engineering, and propose possibilities for improvements.