# Text Simplification of Scientific Texts for Non-Expert Readers

arXiv: https://arxiv.org/abs/2307.03569 · HTML: https://mlsanity.com/paper/2307.03569/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 7 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://mlsanity.com/paper/2112.01488/) (2021)
- [How Language Model Hallucinations Can Snowball](https://mlsanity.com/paper/2305.13534/) (2023)
- [Controllable Sentence Simplification](https://mlsanity.com/paper/1910.02677/) (2019)
- [Learning Rich Representation of Keyphrases from Text](https://mlsanity.com/paper/2112.08547/) (2021)
- [Declarative Experimentation in Information Retrieval using PyTerrier](https://mlsanity.com/paper/2007.14271/) (2020)
- [Controlling Pre-trained Language Models for Grade-Specific Text Simplification](https://mlsanity.com/paper/2305.14993/) (2023)

## Built on by (most cited first, 3 of 3)

- [A Survey of Pre-trained Language Models for Processing Scientific Text](https://mlsanity.com/paper/2401.17824/) (2024)
- [THM@SimpleText 2025 -- Task 1.1: Revisiting Text Simplification based on Complex Terms for Non-Experts](https://mlsanity.com/paper/2507.04414/) (2025)
- [A Human-in-the-Loop Corpus for LLM-Based Simplification of Scientific Summaries](https://mlsanity.com/paper/2607.25630/) (2026)

## Abstract (arXiv)

Reading levels are highly individual and can depend on a text's language, a person's cognitive abilities, or knowledge on a topic. Text simplification is the task of rephrasing a text to better cater to the abilities of a specific target reader group. Simplification of scientific abstracts helps non-experts to access the core information by bypassing formulations that require domain or expert knowledge. This is especially relevant for, e.g., cancer patients reading about novel treatment options. The SimpleText lab hosts the simplification of scientific abstracts for non-experts (Task 3) to advance this field. We contribute three runs employing out-of-the-box summarization models (two based on T5, one based on PEGASUS) and one run using ChatGPT with complex phrase identification.