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ACL Findings2024

BATS: BenchmArking Text Simplicity 🦇

Christin Kreutz, Fabian Haak, Björn Engelmann, Philipp Schaer

Abstract

Evaluation of text simplification currently focuses on the difference between a source text and its simplified variant. Datasets for this evaluation base on a specific topic and group of readers for which is simplified. The broad applicability of text simplification and specifics that come with intended target audiences (e.g., children compared to adult non-experts) are disregarded. An explainable assessment of the overall simplicity of text is missing. This work is BenchmArking Text Simplicity (BATS): we provide an explainable method to assess practical and concrete rules from literature describing features of simplicity and complexity of text. Our experiments on 15 datasets for text simplification highlight differences in features that are important in different domains of text and for different intended target audiences.

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Builds on4

  1. BERTScore: Evaluating Text Generation with BERTTianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger et al.ICLR 2020 · 8,443 citations
  2. LENS: A Learnable Evaluation Metric for Text SimplificationMounica Maddela, Yao Dou, David Heineman, Wei XuACL 2023 · 21 citations
  3. ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting TransformationsFernando Alva-Manchego, Louis Martin, Antoine Bordes, Carolina Scarton et al.ACL 2020 · 12 citations
  4. Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSADavid Heineman, Yao Dou, Mounica Maddela, Wei XuEMNLP 2023 · 5 citations

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  3. Methods for Evaluating the Fluency of Automatically Simplified Texts with Deaf and Hard-of-Hearing Adults at Various Literacy LevelsOliver Alonzo, Jessica Trussell, Matthew Watkins, Sooyeon Lee et al.CHI 2022 · 8 citations
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  5. Keep It Simple: Unsupervised Simplification of Multi-Paragraph TextPhilippe Laban, Tobias Schnabel, Paul N. Bennett, Marti A. HearstACL 2021
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