Hierarchical Ranking Neural Network for Long Document Readability Assessment

Readability assessment aims to evaluate the reading di ffi culty of a text. In recent years, while deep learning technol - ogy has been gradually applied to readability assessment, m ost approaches fail to consider either the length of the text or the ordinal relationship of readability labels. This pap er proposes a bidirectional readability assessment mechan ism that captures contextual information to identify regions w ith rich semantic information in the text, thereby predicti ng the readability level of individual sentences. These sente nce-level labels are then used to assist in predicting the ov erall readability level of the document. Additionally, a pairwis e sorting algorithm is introduced to model the ordinal relationship between readability levels through label subtrac tion. Experimental results on Chinese and English datasets demonstrate that the proposed model achieves competitive p erformance and outperforms other baseline models. Introduction Automatic Text Readability (ARA) research originated in th e early 20th century, aiming to evaluate text reading di ffi culty and assist educators in recommending appropriate rea ding materials for learners [ 1 ]. Readability assessment approaches are generally classified into three paradig ms: human evaluation, co-selection-based analysis, and content-based analysis. Human evaluation involves expert annotation or reader surveys; co-selection methods leverage user interaction data such as reading time or choices [ 2 ]; and content-based approaches infer readability using linguistic, syntactic, or semantic features extracted fro m the text itself. Early studies predominantly relied on experts' subjective evaluations and simple statistical feat ures, such as sentence length and word complexity.