# Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification

arXiv: https://arxiv.org/abs/2007.15823 · HTML: https://mlsanity.com/paper/2007.15823/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2020.

## Built on by (most cited first, 12 of 12)

- [CEFR-Based Sentence Difficulty Annotation and Assessment](https://mlsanity.com/paper/2210.11766/) (2022)
- [On the Ethical Considerations of Text Simplification](https://mlsanity.com/paper/2204.09565/) (2022)
- [Controlling Pre-trained Language Models for Grade-Specific Text Simplification](https://mlsanity.com/paper/2305.14993/) (2023)
- [Generating Educational Materials with Different Levels of Readability using LLMs](https://mlsanity.com/paper/2406.12787/) (2024)
- [Evaluating LLMs for Targeted Concept Simplification for Domain-Specific Texts](https://mlsanity.com/paper/2410.20763/) (2024)
- [(Psycho-)Linguistic Features Meet Transformer Models for Improved Explainable and Controllable Text Simplification](https://mlsanity.com/paper/2212.09848/) (2022)
- [SimpLex: a lexical text simplification architecture](https://mlsanity.com/paper/2304.07002/) (2023)
- [Context-Aware Document Simplification](https://mlsanity.com/paper/2305.06274/) (2023)
- [SiTSE: Sinhala Text Simplification Dataset and Evaluation](https://mlsanity.com/paper/2412.01293/) (2024)
- [Unsupervised Sentence Simplification via Dependency Parsing](https://mlsanity.com/paper/2206.12261/) (2022)
- [Predicting Retrieval Utility and Answer Quality in Retrieval-Augmented Generation](https://mlsanity.com/paper/2601.14546/) (2026)
- [AbLit: A Resource for Analyzing and Generating Abridged Versions of English Literature](https://mlsanity.com/paper/2302.06579/) (2023)

## Abstract (arXiv)

Text simplification reduces the language complexity of professional content for accessibility purposes. End-to-end neural network models have been widely adopted to directly generate the simplified version of input text, usually functioning as a blackbox. We show that text simplification can be decomposed into a compact pipeline of tasks to ensure the transparency and explainability of the process. The first two steps in this pipeline are often neglected: 1) to predict whether a given piece of text needs to be simplified, and 2) if yes, to identify complex parts of the text. The two tasks can be solved separately using either lexical or deep learning methods, or solved jointly. Simply applying explainable complexity prediction as a preliminary step, the out-of-sample text simplification performance of the state-of-the-art, black-box simplification models can be improved by a large margin.