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Related Experiment Video

Updated: Jul 14, 2025

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MeaningBERT: assessing meaning preservation between sentences.

David Beauchemin1, Horacio Saggion2, Richard Khoury1

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Frontiers in Artificial Intelligence
|October 9, 2023
PubMed
Summary
This summary is machine-generated.

Evaluating automatic text simplification requires robust meaning preservation metrics. Existing methods struggle, but a new trainable metric, MeaningBERT, shows strong correlation with human judgment for better text simplification evaluation.

Keywords:
automatic text simplificationevaluation of text simplification systemsfew-shot evaluation of text simplification systemslexical simplificationmeaning preservationsyntactic simplification

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Assessing meaning preservation is crucial in automatic text simplification.
  • Existing n-gram overlap metrics fail with paraphrasing.
  • Current large language model (LLM)-based metrics lack strong correlation with human judgment and haven't been evaluated for text simplification.

Purpose of the Study:

  • To meta-evaluate existing metrics for content similarity in text simplification.
  • To introduce MeaningBERT, a novel trainable metric for assessing meaning preservation in text simplification.
  • To demonstrate MeaningBERT's correlation with human judgment and its versatility across datasets.

Main Methods:

  • Meta-evaluation of several content similarity metrics applied to text simplification.
  • Testing existing metrics against trivial content preservation benchmarks.
  • Development and evaluation of MeaningBERT, a trainable metric for sentence-pair meaning preservation.

Main Results:

  • Existing metrics demonstrate inadequacy, failing simple content preservation tests.
  • MeaningBERT shows a strong correlation with human judgment in assessing meaning preservation.
  • A compilation of datasets for meaning preservation assessment and benchmarking is provided.

Conclusions:

  • Current evaluation metrics are insufficient for text simplification's meaning preservation task.
  • MeaningBERT offers a promising, human-aligned approach for evaluating text simplification.
  • The study provides valuable resources and benchmarks for future research in text simplification evaluation.