# Simple is not Enough: Document-level Text Simplification using Readability and Coherence

arXiv: https://arxiv.org/abs/2412.18655 · HTML: https://mlsanity.com/paper/2412.18655/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2024.

## Built on by (most cited first, 2 of 2)

- [Right at My Level: A Unified Multilingual Framework for Proficiency-Aware Text Simplification](https://mlsanity.com/paper/2604.05302/) (2026)
- [Document-Level Text Simplification in Estonian Using Large Language Models](https://mlsanity.com/paper/2610.10378/) (2026)

## Abstract (arXiv)

In this paper, we present the SimDoc system, a simplification model considering simplicity, readability, and discourse aspects, such as coherence. In the past decade, the progress of the Text Simplification (TS) field has been mostly shown at a sentence level, rather than considering paragraphs or documents, a setting from which most TS audiences would benefit. We propose a simplification system that is initially fine-tuned with professionally created corpora. Further, we include multiple objectives during training, considering simplicity, readability, and coherence altogether. Our contributions include the extension of professionally annotated simplification corpora by the association of existing annotations into (complex text, simple text, readability label) triples to benefit from readability during training. Also, we present a comparative analysis in which we evaluate our proposed models in a zero-shot, few-shot, and fine-tuning setting using document-level TS corpora, demonstrating novel methods for simplification. Finally, we show a detailed analysis of outputs, highlighting the difficulties of simplification at a document level.