# ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysis

arXiv: https://arxiv.org/abs/2103.04083 · HTML: https://mlsanity.com/paper/2103.04083/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2021.

## Built on by (most cited first, 16 of 16)

- [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)
- [A Neural Pairwise Ranking Model for Readability Assessment](https://mlsanity.com/paper/2203.07450/) (2022)
- [SATformer: Transformer-Based UNSAT Core Learning](https://mlsanity.com/paper/2209.00953/) (2022)
- [A Unified Neural Network Model for Readability Assessment with Feature Projection and Length-Balanced Loss](https://mlsanity.com/paper/2210.10305/) (2022)
- [Traditional Readability Formulas Compared for English](https://mlsanity.com/paper/2301.02975/) (2023)
- [Ace-CEFR -- A Dataset for Automated Evaluation of the Linguistic Difficulty of Conversational Texts for LLM Applications](https://mlsanity.com/paper/2506.14046/) (2025)
- [Ling-CL: Understanding NLP Models through Linguistic Curricula](https://mlsanity.com/paper/2310.20121/) (2023)
- [Rethinking the Role of Text Complexity in Language Model Pretraining](https://mlsanity.com/paper/2509.16551/) (2025)
- [A Simple Post-Processing Technique for Improving Readability Assessment of Texts using Word Mover's Distance](https://mlsanity.com/paper/2103.07277/) (2021)
- [FPT: Feature Prompt Tuning for Few-shot Readability Assessment](https://mlsanity.com/paper/2404.02772/) (2024)
- [Eye Tracking Based Cognitive Evaluation of Automatic Readability Assessment Methods](https://mlsanity.com/paper/2502.11150/) (2025)
- [Cross-corpus Readability Compatibility Assessment for English Texts](https://mlsanity.com/paper/2306.09704/) (2023)
- [A study of Vietnamese readability assessing through semantic and statistical features](https://mlsanity.com/paper/2411.04756/) (2024)
- [Hierarchical Ranking Neural Network for Long Document Readability Assessment](https://mlsanity.com/paper/2511.21473/) (2025)

## Abstract (arXiv)

Analyzing the readability of articles has been an important sociolinguistic task. Addressing this task is necessary to the automatic recommendation of appropriate articles to readers with different comprehension abilities, and it further benefits education systems, web information systems, and digital libraries. Current methods for assessing readability employ empirical measures or statistical learning techniques that are limited by their ability to characterize complex patterns such as article structures and semantic meanings of sentences. In this paper, we propose a new and comprehensive framework which uses a hierarchical self-attention model to analyze document readability. In this model, measurements of sentence-level difficulty are captured along with the semantic meanings of each sentence. Additionally, the sentence-level features are incorporated to characterize the overall readability of an article with consideration of article structures. We evaluate our proposed approach on three widely-used benchmark datasets against several strong baseline approaches. Experimental results show that our proposed method achieves the state-of-the-art performance on estimating the readability for various web articles and literature.