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Evaluating Factuality in Text Simplification.

Ashwin Devaraj1, William Sheffield2,3, Byron C Wallace4

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Automated simplification models can make complex texts easier to read but may introduce factual errors. New research highlights these inaccuracies, which current evaluation methods miss, stressing the need for factuality checks in simplification technology.

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Information Accessibility

Background:

  • Automated simplification models aim to improve text readability for broader audiences, such as making medical literature accessible to lay readers.
  • However, these models risk introducing factual inaccuracies, including unsupported statements or omitted information, potentially undermining their utility.
  • The factual accuracy of automated simplification has not been systematically studied, unlike in text summarization.

Purpose of the Study:

  • To investigate the problem of factual accuracy in automated text simplification.
  • To introduce a novel taxonomy for analyzing errors in simplified texts.
  • To evaluate the effectiveness of existing metrics in capturing these errors.

Main Methods:

  • Developed a new taxonomy of errors specific to automated text simplification.
  • Analyzed texts from standard simplification datasets.
  • Evaluated outputs from state-of-the-art simplification models using the new taxonomy.

Main Results:

  • Identified various factual errors in both existing datasets and model outputs.
  • Found that current evaluation metrics often fail to detect these critical inaccuracies.
  • Demonstrated that errors are prevalent and not adequately addressed by existing methods.

Conclusions:

  • Automated simplification models can produce factually inaccurate texts.
  • Existing evaluation metrics are insufficient for assessing the factuality of simplified content.
  • Further research is crucial to develop methods ensuring the factual accuracy of automated simplification systems.