@inproceedings{zhou-etal-2026-simplify,
    title = "Simplify-Pro: A Two-level and Progressive {LLM}-based Framework for Auto Long Text Simplification",
    author = "Zhou, Peng  and
      Li, Guangxin  and
      Huang, Xiaoying  and
      Tang, Yiming",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1112/",
    pages = "22096--22122",
    ISBN = "979-8-89176-395-1",
    abstract = "Text simplification plays a vital role in natural language processing, yet auto long text simplification remains challenging due to the difficulty in the joint balancing of simplification efficiency and fine-grained quality requirements, such as fluency, grammatical correctness and semantic completeness. To address these challenges, we propose Simplify-Pro, a two-level and progressive LLM-based framework that establishes an effective paradigm for automatic long text simplification under diverse test scenarios. By integrating paragraph-level training, simplification generation, metric-assisted analysis and selective refinement into a unified multi-stage pipeline, our framework achieves superior performance across in-domain and out-of-domain simplification tasks, which matches or even outperforms advanced and proprietary LLMs. Furthermore, comprehensive experiments and qualitative analyses cover the simplification performance, generalization ability and the contribution of each individual stage, demonstrating the effectiveness, robustness and modular design advantages of Simplify-Pro."
}