@inproceedings{braun-etal-2022-summary,
title = "Does Summary Evaluation Survive Translation to Other Languages?",
author = "Braun, Spencer and
Vasilyev, Oleg and
Iskender, Neslihan and
Bohannon, John",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.173",
doi = "10.18653/v1/2022.naacl-main.173",
pages = "2425--2435",
abstract = "The creation of a quality summarization dataset is an expensive, time-consuming effort, requiring the production and evaluation of summaries by both trained humans and machines. The returns to such an effort would increase significantly if the dataset could be used in additional languages without repeating human annotations. To investigate how much we can trust machine translation of summarization datasets, we translate the English SummEval dataset to seven languages and compare performances across automatic evaluation measures. We explore equivalence testing as the appropriate statistical paradigm for evaluating correlations between human and automated scoring of summaries. We also consider the effect of translation on the relative performance between measures. We find some potential for dataset reuse in languages similar to the source and along particular dimensions of summary quality. Our code and data can be found at \url{https://github.com/PrimerAI/primer-research/}.",
}
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<abstract>The creation of a quality summarization dataset is an expensive, time-consuming effort, requiring the production and evaluation of summaries by both trained humans and machines. The returns to such an effort would increase significantly if the dataset could be used in additional languages without repeating human annotations. To investigate how much we can trust machine translation of summarization datasets, we translate the English SummEval dataset to seven languages and compare performances across automatic evaluation measures. We explore equivalence testing as the appropriate statistical paradigm for evaluating correlations between human and automated scoring of summaries. We also consider the effect of translation on the relative performance between measures. We find some potential for dataset reuse in languages similar to the source and along particular dimensions of summary quality. Our code and data can be found at https://github.com/PrimerAI/primer-research/.</abstract>
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%0 Conference Proceedings
%T Does Summary Evaluation Survive Translation to Other Languages?
%A Braun, Spencer
%A Vasilyev, Oleg
%A Iskender, Neslihan
%A Bohannon, John
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F braun-etal-2022-summary
%X The creation of a quality summarization dataset is an expensive, time-consuming effort, requiring the production and evaluation of summaries by both trained humans and machines. The returns to such an effort would increase significantly if the dataset could be used in additional languages without repeating human annotations. To investigate how much we can trust machine translation of summarization datasets, we translate the English SummEval dataset to seven languages and compare performances across automatic evaluation measures. We explore equivalence testing as the appropriate statistical paradigm for evaluating correlations between human and automated scoring of summaries. We also consider the effect of translation on the relative performance between measures. We find some potential for dataset reuse in languages similar to the source and along particular dimensions of summary quality. Our code and data can be found at https://github.com/PrimerAI/primer-research/.
%R 10.18653/v1/2022.naacl-main.173
%U https://aclanthology.org/2022.naacl-main.173
%U https://doi.org/10.18653/v1/2022.naacl-main.173
%P 2425-2435
Markdown (Informal)
[Does Summary Evaluation Survive Translation to Other Languages?](https://aclanthology.org/2022.naacl-main.173) (Braun et al., NAACL 2022)
ACL
- Spencer Braun, Oleg Vasilyev, Neslihan Iskender, and John Bohannon. 2022. Does Summary Evaluation Survive Translation to Other Languages?. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2425–2435, Seattle, United States. Association for Computational Linguistics.