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README.md

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-
----
-displayName: SAMSum Corpus
-labelTypes:
-- Classification
-license:
-- CC BY-NC-ND 4.0
-mediaTypes:
-- Text
-paperUrl: https://arxiv.org/pdf/1911.12237v2.pdf
-publishDate: "2019"
-publishUrl: https://github.com/huggingface/datasets/tree/master/datasets/samsum
-publisher:
-- Samsung R&D Institute Poland
-tags:
-- Text
-taskTypes:
-- Text Summarization/Simplication
-- Federated Learning
-- Abstractive Text Summarization
-
----
-# 数据集介绍
-  ## 简介
-   SAMSum 数据集包含大约 16k 个带有摘要的类似信使的对话。对话由精通英语的语言学家创建和记录。语言学家被要求创建类似于他们每天所写的对话，以反映他们现实生活中的信使对话的主题比例。风格和语域是多样化的——对话可以是非正式的、半正式的或正式的，它们可能包含俚语、表情符号和错别字。然后，用摘要对对话进行注释。假设摘要应该是人们在第三人称对话中所谈论内容的简明扼要。 SAMSum 数据集由波兰三星研发研究所准备并分发用于研究目的（非商业许可：CC BY-NC-ND 4.0）。 
-  ## 引文
-  
-```
-"@article{gliwa2019samsum,
-title={SAMSum corpus: A human-annotated dialogue dataset for abstractive summarization},
-author={Gliwa, Bogdan and Mochol, Iwona and Biesek, Maciej and Wawer, Aleksander},
-journal={arXiv preprint arXiv:1911.12237},
-year={2019}
-}"
-```
-
-  
-## Download dataset
-:modelscope-code[]{type="git"}

dataset-samsum-corpus

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-Subproject commit 770dc84e80bf4cd9de9445bac37ace3879389cb9

metafile.yaml

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-displayName: SAMSum Corpus
-labelTypes:
-- Classification
-license:
-- CC BY-NC-ND 4.0
-mediaTypes:
-- Text
-paperUrl: https://arxiv.org/pdf/1911.12237v2.pdf
-publishDate: "2019"
-publishUrl: https://github.com/huggingface/datasets/tree/master/datasets/samsum
-publisher:
-- Samsung R&D Institute Poland
-tags:
-- Text
-taskTypes:
-- Text Summarization/Simplication
-- Federated Learning
-- Abstractive Text Summarization

quickstart.md

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-
-## SDK usage
-```python
-from modelscope.msdatasets import MsDataset
-
-MsDataset.load("OpenDataLab/SAMSum_Corpus")
-
-# Note: If the SDK is not available, please use git to download the dataset.
-```