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GEM/xmediasum

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Use restrictions

The declared licence (cc-by-nc-sa-4.0) is classified as having use restrictions.

Declared licence: cc-by-nc-sa-4.0, from the tagEvidence: README.md at 15296bd
Licence files: No licence file at the top level of the repositoryEvidence: file list at 15296bd
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Tasks

summarization

Citation text from the card

@inproceedings{wang-etal-2022-clidsum,
    title = "{C}lid{S}um: A Benchmark Dataset for Cross-Lingual Dialogue Summarization",
    author = "Wang, Jiaan  and
      Meng, Fandong  and
      Lu, Ziyao  and
      Zheng, Duo  and
      Li, Zhixu  and
      Qu, Jianfeng  and
      Zhou, Jie",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.emnlp-main.526",
    pages = "7716--7729",
    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-t

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dataset_licence({ "id": "GEM/xmediasum" })
signal: "restrictive"
stated_licence: "cc-by-nc-sa-4.0"
sources: 0