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        <title>UoL-UPF at TSAR 2025 Shared Task A Generate-and-Select Approach for Readability-Controlled Text Simplification</title>
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    <abstract>The TSAR 2025 Shared Task on Readability-Controlled Text Simplification focuses on simplifying English paragraphs written at an advanced level (B2 or higher) and rewriting them to target CEFR levels (A2 or B1). The challenge is to reduce linguistic complexity without sacrificing coherence or meaning. We developed three complementary approaches based on large language models (LLMs). The first approach (Run 1) generates a diverse set of paragraph-level simplifications. It then applies filters to enforce CEFR alignment, preserve meaning, and encourage diversity, and finally selects the candidates with the lowest perceived risk. The second (Run 2) performs simplification at the sentence level, combining structured prompting, coreference resolution, and explainable AI techniques to highlight influential phrases, with candidate selection guided by automatic and LLM-based judges. The third hybrid approach (Run 3) integrates both strategies by pooling paragraph- and sentence-level simplifications, and subsequently applying the identical filtering and selection architecture used in Run 1. In the official TSAR evaluation, the hybrid system ranked 2nd overall, while its component systems also achieved competitive results.</abstract>
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%0 Conference Proceedings
%T UoL-UPF at TSAR 2025 Shared Task A Generate-and-Select Approach for Readability-Controlled Text Simplification
%A Hayakawa, Akio
%A Khallaf, Nouran
%A Saggion, Horacio
%A Sharoff, Serge
%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 hayakawa-etal-2025-uol
%X The TSAR 2025 Shared Task on Readability-Controlled Text Simplification focuses on simplifying English paragraphs written at an advanced level (B2 or higher) and rewriting them to target CEFR levels (A2 or B1). The challenge is to reduce linguistic complexity without sacrificing coherence or meaning. We developed three complementary approaches based on large language models (LLMs). The first approach (Run 1) generates a diverse set of paragraph-level simplifications. It then applies filters to enforce CEFR alignment, preserve meaning, and encourage diversity, and finally selects the candidates with the lowest perceived risk. The second (Run 2) performs simplification at the sentence level, combining structured prompting, coreference resolution, and explainable AI techniques to highlight influential phrases, with candidate selection guided by automatic and LLM-based judges. The third hybrid approach (Run 3) integrates both strategies by pooling paragraph- and sentence-level simplifications, and subsequently applying the identical filtering and selection architecture used in Run 1. In the official TSAR evaluation, the hybrid system ranked 2nd overall, while its component systems also achieved competitive results.
%U https://aclanthology.org/2025.tsar-1.16/
%P 193-210

Markdown (Informal)

[UoL-UPF at TSAR 2025 Shared Task A Generate-and-Select Approach for Readability-Controlled Text Simplification](https://aclanthology.org/2025.tsar-1.16/) (Hayakawa et al., TSAR 2025)

• UoL-UPF at TSAR 2025 Shared Task A Generate-and-Select Approach for Readability-Controlled Text Simplification (Hayakawa et al., TSAR 2025)

ACL

• Akio Hayakawa, Nouran Khallaf, Horacio Saggion, and Serge Sharoff. 2025. UoL-UPF at TSAR 2025 Shared Task A Generate-and-Select Approach for Readability-Controlled Text Simplification. In Proceedings of the Fourth Workshop on Text Simplification, Accessibility and Readability (TSAR 2025), pages 193–210, Suzhou, China. Association for Computational Linguistics.