Hierarchical Ranking Neural Network for Long Document Readability Assessment

TL;DR

This paper introduces a hierarchical neural network that assesses long document readability by capturing sentence-level semantics and modeling the ordinal nature of readability levels, improving prediction accuracy.

Contribution

It proposes a bidirectional mechanism for sentence-level readability prediction and a pairwise sorting algorithm to model ordinal relationships, addressing limitations of previous methods.

Findings

Achieves competitive performance on Chinese and English datasets.

Outperforms baseline models in readability assessment.

Effectively models the ordinal relationship of readability levels.

Abstract

Readability assessment aims to evaluate the reading difficulty of a text. In recent years, while deep learning technology has been gradually applied to readability assessment, most approaches fail to consider either the length of the text or the ordinal relationship of readability labels. This paper proposes a bidirectional readability assessment mechanism that captures contextual information to identify regions with rich semantic information in the text, thereby predicting the readability level of individual sentences. These sentence-level labels are then used to assist in predicting the overall readability level of the document. Additionally, a pairwise sorting algorithm is introduced to model the ordinal relationship between readability levels through label subtraction. Experimental results on Chinese and English datasets demonstrate that the proposed model achieves competitive performance and outperforms other baseline models.