@inproceedings{mathas-etal-2026-plan,
title = "Plan-Guided Text Simplification with Extended Contexts",
author = "Mathas, Pascal and
Bakker, Jan and
Kamps, Jaap",
editor = "Shardlow, Matthew and
Fran{\c{c}}ois, Thomas and
Amaro, Raquel and
Baptista, Jorge and
Cardon, R{\'e}mi and
Ribeiro, Eug{\'e}nio and
Saggion, Horacio and
Stodden, Regina and
Todirascu, Amalia and
Wilkens, Rodrigo",
booktitle = "Proceedings of the Joint Workshop on Readability and Text Simplification ({READI}x{TSAR}) @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.readi-1.9/",
doi = "10.63317/2gk3wxp2d525",
pages = "121--129",
abstract = "In this paper, we investigate the impact of increasing context lengths (one to five paragraphs) on plan-following accuracy in plan-guided text simplification. Plan-guided models simplify text according to sentence-level operation labels such as copy, rephrase, split, and delete. Previous work fine-tunes BART with target reading-level and sentence-level operation tokens to perform this task. We find that BART{'}s plan-following accuracy on Newsela-auto drops significantly as context increases from one to five paragraphs. This means that the model becomes less reliable with longer contexts, and the quality of its outputs decreases. To address this, we propose replacing the fine-tuned BART models with a prompting-based approach using instruction-tuned Qwen models. We find that this approach not only maintains robust plan-following across all context lengths, but even at the longest context length still exceeds BART{'}s performance at the shortest. We further provide ablations on model size and model family, showing that a minimum model capacity is required for the approach to work and that it transfers across LLM families."
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<abstract>In this paper, we investigate the impact of increasing context lengths (one to five paragraphs) on plan-following accuracy in plan-guided text simplification. Plan-guided models simplify text according to sentence-level operation labels such as copy, rephrase, split, and delete. Previous work fine-tunes BART with target reading-level and sentence-level operation tokens to perform this task. We find that BART’s plan-following accuracy on Newsela-auto drops significantly as context increases from one to five paragraphs. This means that the model becomes less reliable with longer contexts, and the quality of its outputs decreases. To address this, we propose replacing the fine-tuned BART models with a prompting-based approach using instruction-tuned Qwen models. We find that this approach not only maintains robust plan-following across all context lengths, but even at the longest context length still exceeds BART’s performance at the shortest. We further provide ablations on model size and model family, showing that a minimum model capacity is required for the approach to work and that it transfers across LLM families.</abstract>
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%0 Conference Proceedings
%T Plan-Guided Text Simplification with Extended Contexts
%A Mathas, Pascal
%A Bakker, Jan
%A Kamps, Jaap
%Y Shardlow, Matthew
%Y François, Thomas
%Y Amaro, Raquel
%Y Baptista, Jorge
%Y Cardon, Rémi
%Y Ribeiro, Eugénio
%Y Saggion, Horacio
%Y Stodden, Regina
%Y Todirascu, Amalia
%Y Wilkens, Rodrigo
%S Proceedings of the Joint Workshop on Readability and Text Simplification (READIxTSAR) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F mathas-etal-2026-plan
%X In this paper, we investigate the impact of increasing context lengths (one to five paragraphs) on plan-following accuracy in plan-guided text simplification. Plan-guided models simplify text according to sentence-level operation labels such as copy, rephrase, split, and delete. Previous work fine-tunes BART with target reading-level and sentence-level operation tokens to perform this task. We find that BART’s plan-following accuracy on Newsela-auto drops significantly as context increases from one to five paragraphs. This means that the model becomes less reliable with longer contexts, and the quality of its outputs decreases. To address this, we propose replacing the fine-tuned BART models with a prompting-based approach using instruction-tuned Qwen models. We find that this approach not only maintains robust plan-following across all context lengths, but even at the longest context length still exceeds BART’s performance at the shortest. We further provide ablations on model size and model family, showing that a minimum model capacity is required for the approach to work and that it transfers across LLM families.
%R 10.63317/2gk3wxp2d525
%U https://aclanthology.org/2026.readi-1.9/
%U https://doi.org/10.63317/2gk3wxp2d525
%P 121-129
Markdown (Informal)
[Plan-Guided Text Simplification with Extended Contexts](https://aclanthology.org/2026.readi-1.9/) (Mathas et al., READI-TSAR 2026)
ACL
- Pascal Mathas, Jan Bakker, and Jaap Kamps. 2026. Plan-Guided Text Simplification with Extended Contexts. In Proceedings of the Joint Workshop on Readability and Text Simplification (READIxTSAR) @ LREC 2026, pages 121–129, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).