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        <title>BATS: BenchmArking Text Simplicity 🦇</title>
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    <name type="personal">
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            <namePart type="given">Lun-Wei</namePart>
            <namePart type="family">Ku</namePart>
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        <name type="personal">
            <namePart type="given">Andre</namePart>
            <namePart type="family">Martins</namePart>
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        <name type="personal">
            <namePart type="given">Vivek</namePart>
            <namePart type="family">Srikumar</namePart>
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    <abstract>Evaluation of text simplification currently focuses on the difference of a source text to 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.</abstract>
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    <identifier type="doi">10.18653/v1/2024.findings-acl.712</identifier>
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%0 Conference Proceedings
%T BATS: BenchmArking Text Simplicity 🦇
%A Kreutz, Christin
%A Haak, Fabian
%A Engelmann, Björn
%A Schaer, Philipp
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F kreutz-etal-2024-bats
%X Evaluation of text simplification currently focuses on the difference of a source text to 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.
%R 10.18653/v1/2024.findings-acl.712
%U https://aclanthology.org/2024.findings-acl.712
%U https://doi.org/10.18653/v1/2024.findings-acl.712
%P 11968-11989

Markdown (Informal)

[BATS: BenchmArking Text Simplicity 🦇](https://aclanthology.org/2024.findings-acl.712) (Kreutz et al., Findings 2024)

• BATS: BenchmArking Text Simplicity 🦇 (Kreutz et al., Findings 2024)

ACL

• Christin Kreutz, Fabian Haak, Björn Engelmann, and Philipp Schaer. 2024. BATS: BenchmArking Text Simplicity 🦇. In Findings of the Association for Computational Linguistics ACL 2024, pages 11968–11989, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.