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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.21473 |
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| _version_ | 1866915639335059456 |
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| author | Zheng, Yurui Chen, Yijun Zhang, Shaohong |
| author_facet | Zheng, Yurui Chen, Yijun Zhang, Shaohong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21473 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hierarchical Ranking Neural Network for Long Document Readability Assessment Zheng, Yurui Chen, Yijun Zhang, Shaohong Computation and Language Artificial Intelligence 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. |
| title | Hierarchical Ranking Neural Network for Long Document Readability Assessment |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2511.21473 |