Readability-controlled text modification aims to rewrite an input text so that it reaches a target level of difficulty. This task is closely related to automatic readability assessment (ARA) since, depending on the difficulty level of the input text, it may need to be simplified or complexified. Most previous research in LLM-based text modification has focused on zero-shot prompting, without further input from ARA or guidance on text spans that most likely require revision. This paper shows that ARA models for texts and sentences, as well as predictions of text spans that should be edited, can enhance performance in readability-controlled text modification.

Sentence Simplification aims to make sentences easier to read and understand. With most effort on corpus development focused on English, the amount of annotated data is limited in Chinese. To address this need, we introduce CSSWiki, an open-source dataset for Chinese sentence simplification based on Wikipedia. This dataset contains 1.6k source sentences paired with their simplified versions. Each sentence pair is annotated with operation tags that distinguish between linguistic and content modifications. We analyze differences in annotation scheme and data statistics between CSSWiki and existing datasets. We then report baseline sentence simplification performance on CSSWiki using zero-shot and few-shot approaches with Large Language Models.

Automatic readability assessment (ARA) predicts how difficult it is for the reader to understand a text. While ARA has traditionally been performed at the passage level, there has been increasing interest in ARA at the sentence level, given its applications in downstream tasks such as text simplification and language exercise generation. Recent research has suggested the effectiveness of hybrid approaches for ARA, but they have yet to be applied on the sentence level. We present the first study that compares neural and hybrid models for sentence-level ARA. We conducted experiments on graded sentences from the Wall Street Journal (WSJ) and a dataset derived from the OneStopEnglish corpus. Experimental results show that both neural and hybrid models outperform traditional classifiers trained on linguistic features. Hybrid models obtained the best accuracy on both datasets, surpassing the previous best result reported on the WSJ dataset by almost 13% absolute.