# LLM-Guided Planning and Summary-Based Scientific Text Simplification: DS@GT at CLEF 2025 SimpleText

arXiv: https://arxiv.org/abs/2508.11816 · HTML: https://mlsanity.com/paper/2508.11816/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 15 Aug 2025.

## Builds on (most cited first, 2 of 2)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Progressive Document-level Text Simplification via Large Language Models](https://mlsanity.com/paper/2501.03857/) (2025)

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

- [The Impact of AI-Generated Text on the Internet](https://mlsanity.com/paper/2604.26965/) (2026)
- [Template-Based Text-to-Image Alignment for Language Accessibility: A Study on Visualizing Text Simplifications](https://mlsanity.com/paper/2510.11314/) (2025)

## Abstract (arXiv)

In this paper, we present our approach for the CLEF 2025 SimpleText Task 1, which addresses both sentence-level and document-level scientific text simplification. For sentence-level simplification, our methodology employs large language models (LLMs) to first generate a structured plan, followed by plan-driven simplification of individual sentences. At the document level, we leverage LLMs to produce concise summaries and subsequently guide the simplification process using these summaries. This two-stage, LLM-based framework enables more coherent and contextually faithful simplifications of scientific text.