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        <title>Rethinking Efficient Multilingual Text Summarization Meta-Evaluation</title>
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        <namePart type="given">Rilyn</namePart>
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        <namePart type="given">Jiawen</namePart>
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    <abstract>Evaluating multilingual summarization evaluation metrics, i.e., meta-evaluation, is challenging because of the difficulty of human annotation collection. Therefore, we investigate an efficient multilingual meta-evaluation framework that uses machine translation systems to transform a monolingual meta-evaluation dataset into multilingual versions. To this end, we introduce a statistical test to verify the transformed dataset quality by checking the meta-evaluation result consistency on the original dataset and back-translated dataset. With this quality verification method, we transform an existing English summarization meta-evaluation dataset, RoSE, into 30 languages, and conduct a multilingual meta-evaluation of several representative automatic evaluation metrics. In our meta-evaluation, we find that metric performance varies in different languages and neural metrics generally outperform classical text-matching-based metrics in non-English languages. Moreover, we identify a two-stage evaluation method with superior performance, which first translates multilingual texts into English and then performs evaluation. We make the transformed datasets publicly available to facilitate future research.</abstract>
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    <identifier type="doi">10.18653/v1/2024.findings-acl.930</identifier>
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%0 Conference Proceedings
%T Rethinking Efficient Multilingual Text Summarization Meta-Evaluation
%A Han, Rilyn
%A Chen, Jiawen
%A Liu, Yixin
%A Cohan, Arman
%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
%F han-etal-2024-rethinking
%X Evaluating multilingual summarization evaluation metrics, i.e., meta-evaluation, is challenging because of the difficulty of human annotation collection. Therefore, we investigate an efficient multilingual meta-evaluation framework that uses machine translation systems to transform a monolingual meta-evaluation dataset into multilingual versions. To this end, we introduce a statistical test to verify the transformed dataset quality by checking the meta-evaluation result consistency on the original dataset and back-translated dataset. With this quality verification method, we transform an existing English summarization meta-evaluation dataset, RoSE, into 30 languages, and conduct a multilingual meta-evaluation of several representative automatic evaluation metrics. In our meta-evaluation, we find that metric performance varies in different languages and neural metrics generally outperform classical text-matching-based metrics in non-English languages. Moreover, we identify a two-stage evaluation method with superior performance, which first translates multilingual texts into English and then performs evaluation. We make the transformed datasets publicly available to facilitate future research.
%R 10.18653/v1/2024.findings-acl.930
%U https://aclanthology.org/2024.findings-acl.930
%U https://doi.org/10.18653/v1/2024.findings-acl.930
%P 15739-15746

Markdown (Informal)

[Rethinking Efficient Multilingual Text Summarization Meta-Evaluation](https://aclanthology.org/2024.findings-acl.930) (Han et al., Findings 2024)

• Rethinking Efficient Multilingual Text Summarization Meta-Evaluation (Han et al., Findings 2024)

ACL

• Rilyn Han, Jiawen Chen, Yixin Liu, and Arman Cohan. 2024. Rethinking Efficient Multilingual Text Summarization Meta-Evaluation. In Findings of the Association for Computational Linguistics: ACL 2024, pages 15739–15746, Bangkok, Thailand. Association for Computational Linguistics.