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        <title>Document-level Simplification and Illustration Generation Multimodal Coherence</title>
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    <abstract>We present a novel method for document-level text simplification and automatic illustration generation aimed at enhancing information accessibility for individuals with cognitive impairments. While prior research has primarily focused on sentence- or paragraph-level simplification and text-to-image generation for narrative contexts this work addresses the unique challenges of simplifying long-form documents and generating semantically aligned visuals. The pipeline consists of three stages (1) discourse-aware segmentation using large language models (2) visually grounded description generation via abstraction and (3) controlled image synthesis using state-of-the-art diffusion models including DALLE 3 and FLUX1-dev. We further incorporate stylistic constraints to ensure visual coherence and we conduct a human evaluation measuring comprehension semantic alignment and visual clarity. Experimental results demonstrate that our method effectively combines simplified text and visual content with generated illustrations enhancing textual accessibility.</abstract>
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%0 Conference Proceedings
%T Document-level Simplification and Illustration Generation Multimodal Coherence
%A Liu, Yuhang
%A Zhang, Mo
%A Cheng, Zhaoyi
%A Ebling, Sarah
%Y Shardlow, Matthew
%Y Alva-Manchego, Fernando
%Y North, Kai
%Y Stodden, Regina
%Y Saggion, Horacio
%Y Khallaf, Nouran
%Y Hayakawa, Akio
%S Proceedings of the Fourth Workshop on Text Simplification, Accessibility and Readability (TSAR 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-176-6
%F liu-etal-2025-document
%X We present a novel method for document-level text simplification and automatic illustration generation aimed at enhancing information accessibility for individuals with cognitive impairments. While prior research has primarily focused on sentence- or paragraph-level simplification and text-to-image generation for narrative contexts this work addresses the unique challenges of simplifying long-form documents and generating semantically aligned visuals. The pipeline consists of three stages (1) discourse-aware segmentation using large language models (2) visually grounded description generation via abstraction and (3) controlled image synthesis using state-of-the-art diffusion models including DALLE 3 and FLUX1-dev. We further incorporate stylistic constraints to ensure visual coherence and we conduct a human evaluation measuring comprehension semantic alignment and visual clarity. Experimental results demonstrate that our method effectively combines simplified text and visual content with generated illustrations enhancing textual accessibility.
%R 10.18653/v1/2025.tsar-1.2
%U https://aclanthology.org/2025.tsar-1.2/
%U https://doi.org/10.18653/v1/2025.tsar-1.2
%P 19-35

Markdown (Informal)

[Document-level Simplification and Illustration Generation Multimodal Coherence](https://aclanthology.org/2025.tsar-1.2/) (Liu et al., TSAR 2025)

• Document-level Simplification and Illustration Generation Multimodal Coherence (Liu et al., TSAR 2025)

ACL

• Yuhang Liu, Mo Zhang, Zhaoyi Cheng, and Sarah Ebling. 2025. Document-level Simplification and Illustration Generation Multimodal Coherence. In Proceedings of the Fourth Workshop on Text Simplification, Accessibility and Readability (TSAR 2025), pages 19–35, Suzhou, China. Association for Computational Linguistics.