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    <abstract>“Readability assessment for book-level long text is widely needed in real educational applica-tions. However, most of the current researches focus on passage-level readability assessmentand little work has been done to process ultra-long texts. In order to process the long sequenceof book texts better and to enhance pretrained models with difficulty knowledge, we propose anovel model DSDR, difficulty-aware segment pre-training and difficulty multi-view representa-tion. Specifically, we split all books into multiple fixed-length segments and employ unsuper-vised clustering to obtain difficulty-aware segments, which are used to re-train the pretrainedmodel to learn difficulty knowledge. Accordingly, a long text is represented by averaging mul-tiple vectors of segments with varying difficulty levels. We construct a new dataset of GradedChildren’s Books to evaluate model performance. Our proposed model achieves promising re-sults, outperforming both the traditional SVM classifier and several popular pretrained models.In addition, our work establishes a new prototype for book-level readability assessment, whichprovides an important benchmark for related research in future work.”</abstract>
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%0 Conference Proceedings
%T Going Beyond Passages: Readability Assessment for Book-level Long Texts
%A Li, Wenbiao
%A Sun, Rui
%A Zhang, Tianyi
%A Wu, Yunfang
%Y Maosong, Sun
%Y Jiye, Liang
%Y Xianpei, Han
%Y Zhiyuan, Liu
%Y Yulan, He
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G eng
%F wenbiao-etal-2024-going
%X “Readability assessment for book-level long text is widely needed in real educational applica-tions. However, most of the current researches focus on passage-level readability assessmentand little work has been done to process ultra-long texts. In order to process the long sequenceof book texts better and to enhance pretrained models with difficulty knowledge, we propose anovel model DSDR, difficulty-aware segment pre-training and difficulty multi-view representa-tion. Specifically, we split all books into multiple fixed-length segments and employ unsuper-vised clustering to obtain difficulty-aware segments, which are used to re-train the pretrainedmodel to learn difficulty knowledge. Accordingly, a long text is represented by averaging mul-tiple vectors of segments with varying difficulty levels. We construct a new dataset of GradedChildren’s Books to evaluate model performance. Our proposed model achieves promising re-sults, outperforming both the traditional SVM classifier and several popular pretrained models.In addition, our work establishes a new prototype for book-level readability assessment, whichprovides an important benchmark for related research in future work.”
%U https://aclanthology.org/2024.ccl-1.100/
%P 1298-1309

Markdown (Informal)

[Going Beyond Passages: Readability Assessment for Book-level Long Texts](https://aclanthology.org/2024.ccl-1.100/) (Li et al., CCL 2024)

• Going Beyond Passages: Readability Assessment for Book-level Long Texts (Li et al., CCL 2024)

ACL

• Wenbiao Li, Rui Sun, Tianyi Zhang, and Yunfang Wu. 2024. Going Beyond Passages: Readability Assessment for Book-level Long Texts. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference), pages 1298–1309, Taiyuan, China. Chinese Information Processing Society of China.