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Simplify-Pro: A Two-level and Progressive LLM-based Framework for Auto Long Text Simplification

Summary

Simplify-Pro addresses automatic simplification of long texts. Earlier approaches usually depend on large parallel corpora, and they degrade as inputs grow: they drop key content, distort meaning, and lose coherence. The authors build a modular four-stage pipeline around Qwen2.5-14B-Instruct, which is trained only on a modest amount of paragraph-level data using GRPO. The reward combines a format check, a penalty that keeps reasoning length within bounds, and quality signals from SARI, BERTScore and LENS.

Paragraphs are simplified directly. Documents are first split into semantically coherent blocks using sentence-embedding similarity and length-constrained merging. Each block is simplified separately, and the results are joined back together. An evaluator then describes each candidate output with edit proportions, Levenshtein similarity and lexical complexity. Finally, an LLM editor uses these analyses to pick the best candidate and polish its wording.

On in-domain and out-of-domain tests, the framework performs on par with or better than large proprietary LLMs. Its edit patterns are also closer to human references. This makes the approach attractive for settings where little annotated data is available.

Key points

  • Three reward configurations (Simplify-Pro-RS, -RD, -RT) mix lexical-edit (SARI), semantic (BERTScore) and human-preference (LENS) rewards in different combinations.
  • In a human evaluation of 80 instances (20 from each of four datasets), Simplify-Pro-RT scored highest on Simplicity (4.58) and was preferred most often (19/80). The runner-up was Gemini-3-Pro-Preview (4.25; 16/80).
  • Simplify-Pro-RT ranked second on Meaning Preservation (4.08), just behind DeepSeek-v3.2 (4.23). GPT-5.2 was also among the compared baselines.
  • In the ablation, removing metric-assisted analysis lowered Medical-domain paragraph SARI from 44.64 to 44.03 and D-SARI from 36.19 to 35.39. Also removing selective refinement led to further drops on BERTScore and LENS.
  • Edits were grouped into five categories, and semantic edits made up the largest share. Evaluation covers English only, so multilingual and low-resource settings remain future work.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Machine translation · Natural language processing

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.1112

@inproceedings{zhou2026simplify,
  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},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1112},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1112}
}

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