@inproceedings{wenbiao-etal-2024-going,
    title = "Going Beyond Passages: Readability Assessment for Book-level Long Texts",
    author = "Li, Wenbiao  and
      Sun, Rui  and
      Zhang, Tianyi  and
      Wu, Yunfang",
    editor = "Maosong, Sun  and
      Jiye, Liang  and
      Xianpei, Han  and
      Zhiyuan, Liu  and
      Yulan, He",
    booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
    month = jul,
    year = "2024",
    address = "Taiyuan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/author-degibert/2024.ccl-1.100/",
    pages = "1298--1309",
    language = "eng",
    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.''"
}