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    <abstract>We present ClidSum, a benchmark dataset towards building cross-lingual summarization systems on dialogue documents. It consists of 67k+ dialogue documents and 112k+ annotated summaries in different target languages. Based on the proposed ClidSum, we introduce two benchmark settings for supervised and semi-supervised scenarios, respectively. We then build various baseline systems in different paradigms (pipeline and end-to-end) and conduct extensive experiments on ClidSum to provide deeper analyses. Furthermore, we propose mDialBART which extends mBART via further pre-training, where the multiple objectives help the pre-trained model capture the structural characteristics as well as key content in dialogues and the transformation from source to the target language. Experimental results show the superiority of mDialBART, as an end-to-end model, outperforms strong pipeline models on ClidSum. Finally, we discuss specific challenges that current approaches faced with this task and give multiple promising directions for future research. We have released the dataset and code at https://github.com/krystalan/ClidSum.</abstract>
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%0 Conference Proceedings
%T ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization
%A Wang, Jiaan
%A Meng, Fandong
%A Lu, Ziyao
%A Zheng, Duo
%A Li, Zhixu
%A Qu, Jianfeng
%A Zhou, Jie
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F wang-etal-2022-clidsum
%X We present ClidSum, a benchmark dataset towards building cross-lingual summarization systems on dialogue documents. It consists of 67k+ dialogue documents and 112k+ annotated summaries in different target languages. Based on the proposed ClidSum, we introduce two benchmark settings for supervised and semi-supervised scenarios, respectively. We then build various baseline systems in different paradigms (pipeline and end-to-end) and conduct extensive experiments on ClidSum to provide deeper analyses. Furthermore, we propose mDialBART which extends mBART via further pre-training, where the multiple objectives help the pre-trained model capture the structural characteristics as well as key content in dialogues and the transformation from source to the target language. Experimental results show the superiority of mDialBART, as an end-to-end model, outperforms strong pipeline models on ClidSum. Finally, we discuss specific challenges that current approaches faced with this task and give multiple promising directions for future research. We have released the dataset and code at https://github.com/krystalan/ClidSum.
%R 10.18653/v1/2022.emnlp-main.526
%U https://aclanthology.org/2022.emnlp-main.526
%U https://doi.org/10.18653/v1/2022.emnlp-main.526
%P 7716-7729

Markdown (Informal)

[ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization](https://aclanthology.org/2022.emnlp-main.526) (Wang et al., EMNLP 2022)

• ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization (Wang et al., EMNLP 2022)

ACL

• Jiaan Wang, Fandong Meng, Ziyao Lu, Duo Zheng, Zhixu Li, Jianfeng Qu, and Jie Zhou. 2022. ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 7716–7729, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.