ACL Findings2024
BATS: BenchmArking Text Simplicity 🦇
Christin Kreutz, Fabian Haak, Björn Engelmann, Philipp Schaer
摘要
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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它引用的 Paper4
- BERTScore: Evaluating Text Generation with BERTTianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger 等ICLR 2020 · 8,443 次引用
- LENS: A Learnable Evaluation Metric for Text SimplificationMounica Maddela, Yao Dou, David Heineman, Wei XuACL 2023 · 21 次引用
- ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting TransformationsFernando Alva-Manchego, Louis Martin, Antoine Bordes, Carolina Scarton 等ACL 2020 · 12 次引用
- Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSADavid Heineman, Yao Dou, Mounica Maddela, Wei XuEMNLP 2023 · 5 次引用
相关 Paper
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- Comparison of Methods for Evaluating Complexity of Simplified Texts among Deaf and Hard-of-Hearing Adults at Different Literacy LevelsOliver Alonzo, Jessica Trussell, Becca Dingman, Matt HuenerfauthCHI 2021 · 16 次引用
- Keep It Simple: Unsupervised Simplification of Multi-Paragraph TextPhilippe Laban, Tobias Schnabel, Paul N. Bennett, Marti A. HearstACL 2021
