Text Simplification of Scientific Texts for Non-Expert Readers
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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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 10 of its 27 references that are in the map. First posted on arXiv 7 Jul 2023.
Builds on
10 of 27 references.
References · 10
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
- 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
- 2021ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interactioncited by 464
- 2023How Language Model Hallucinations Can Snowballcited by 310
- 2019Controllable Sentence Simplificationcited by 78
- 2021Learning Rich Representation of Keyphrases from Textcited by 36
- 2020Declarative Experimentation in Information Retrieval using PyTerriercited by 29
- 2023Controlling Pre-trained Language Models for Grade-Specific Text Simplificationcited by 14
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Citing papers per year: 2024: 1; 2025: 1; 2026: 1.
3 papers.