@inproceedings{gao-etal-2023-evaluating,
title = "Evaluating Factuality in Cross-lingual Summarization",
author = "Gao, Mingqi and
Wang, Wenqing and
Wan, Xiaojun and
Xu, Yuemei",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.786",
doi = "10.18653/v1/2023.findings-acl.786",
pages = "12415--12431",
abstract = "Cross-lingual summarization aims to help people efficiently grasp the core idea of the document written in a foreign language. Modern text summarization models generate highly fluent but often factually inconsistent outputs, which has received heightened attention in recent research. However, the factual consistency of cross-lingual summarization has not been investigated yet. In this paper, we propose a cross-lingual factuality dataset by collecting human annotations of reference summaries as well as generated summaries from models at both summary level and sentence level. Furthermore, we perform the fine-grained analysis and observe that over 50{\%} of generated summaries and over 27{\%} of reference summaries contain factual errors with characteristics different from monolingual summarization. Existing evaluation metrics for monolingual summarization require translation to evaluate the factuality of cross-lingual summarization and perform differently at different tasks and levels. Finally, we adapt the monolingual factuality metrics as an initial step towards the automatic evaluation of summarization factuality in cross-lingual settings. Our dataset and code are available at \url{https://github.com/kite99520/Fact_CLS}.",
}
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<abstract>Cross-lingual summarization aims to help people efficiently grasp the core idea of the document written in a foreign language. Modern text summarization models generate highly fluent but often factually inconsistent outputs, which has received heightened attention in recent research. However, the factual consistency of cross-lingual summarization has not been investigated yet. In this paper, we propose a cross-lingual factuality dataset by collecting human annotations of reference summaries as well as generated summaries from models at both summary level and sentence level. Furthermore, we perform the fine-grained analysis and observe that over 50% of generated summaries and over 27% of reference summaries contain factual errors with characteristics different from monolingual summarization. Existing evaluation metrics for monolingual summarization require translation to evaluate the factuality of cross-lingual summarization and perform differently at different tasks and levels. Finally, we adapt the monolingual factuality metrics as an initial step towards the automatic evaluation of summarization factuality in cross-lingual settings. Our dataset and code are available at https://github.com/kite99520/Fact_CLS.</abstract>
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%0 Conference Proceedings
%T Evaluating Factuality in Cross-lingual Summarization
%A Gao, Mingqi
%A Wang, Wenqing
%A Wan, Xiaojun
%A Xu, Yuemei
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F gao-etal-2023-evaluating
%X Cross-lingual summarization aims to help people efficiently grasp the core idea of the document written in a foreign language. Modern text summarization models generate highly fluent but often factually inconsistent outputs, which has received heightened attention in recent research. However, the factual consistency of cross-lingual summarization has not been investigated yet. In this paper, we propose a cross-lingual factuality dataset by collecting human annotations of reference summaries as well as generated summaries from models at both summary level and sentence level. Furthermore, we perform the fine-grained analysis and observe that over 50% of generated summaries and over 27% of reference summaries contain factual errors with characteristics different from monolingual summarization. Existing evaluation metrics for monolingual summarization require translation to evaluate the factuality of cross-lingual summarization and perform differently at different tasks and levels. Finally, we adapt the monolingual factuality metrics as an initial step towards the automatic evaluation of summarization factuality in cross-lingual settings. Our dataset and code are available at https://github.com/kite99520/Fact_CLS.
%R 10.18653/v1/2023.findings-acl.786
%U https://aclanthology.org/2023.findings-acl.786
%U https://doi.org/10.18653/v1/2023.findings-acl.786
%P 12415-12431
Markdown (Informal)
[Evaluating Factuality in Cross-lingual Summarization](https://aclanthology.org/2023.findings-acl.786) (Gao et al., Findings 2023)
ACL
- Mingqi Gao, Wenqing Wang, Xiaojun Wan, and Yuemei Xu. 2023. Evaluating Factuality in Cross-lingual Summarization. In Findings of the Association for Computational Linguistics: ACL 2023, pages 12415–12431, Toronto, Canada. Association for Computational Linguistics.