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Summary of Health Text Simplification: An Annotated Corpus For Digestive Cancer Education and Novel Strategies For Reinforcement Learning, by Md Mushfiqur Rahman et al.


Health Text Simplification: An Annotated Corpus for Digestive Cancer Education and Novel Strategies for Reinforcement Learning

by Md Mushfiqur Rahman, Mohammad Sabik Irbaz, Kai North, Michelle S. Williams, Marcos Zampieri, Kevin Lybarger

First submitted to arxiv on: 26 Jan 2024

Categories

  • Main: Computation and Language (cs.CL)
  • Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

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GrooveSquid.com Paper Summaries

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
The paper aims to develop high-performing text simplification models to improve the understandability and accessibility of health educational materials, specifically for minoritized populations. The current state of patient educational resources often surpasses widely accepted standards in terms of reading level and complexity, leading to a critical need for effective solutions. In cancer education, where prevention and screening education can significantly reduce morbidity and mortality, the importance of this issue is particularly acute.
Low GrooveSquid.com (original content) Low Difficulty Summary
The paper wants to make health information easier to understand by creating better text simplification models. Right now, many educational resources are too hard for some people to read, especially minority groups. This makes it hard for them to learn about important health issues like cancer prevention and screening. The goal is to create better models that can help make health education more accessible and understandable.

Keywords

* Artificial intelligence