主数据卡片链接: GEM Website 。
OrangeSum 是一个受 XSum 启发的法语摘要数据集。它包含两个子任务:摘要生成和标题生成。数据来自 "Orange Actu" 在2011年至2020年期间的文章。
您可以通过以下方式加载数据集:
import datasets data = datasets.load_dataset('GEM/OrangeSum')
数据加载器可在此找到: here 。
paper@inproceedings{kamal-eddine-etal-2021-barthez, title = "{BART}hez: a Skilled Pretrained {F}rench Sequence-to-Sequence Model", author = "Kamal Eddine, Moussa and Tixier, Antoine and Vazirgiannis, Michalis", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.740", doi = "10.18653/v1/2021.emnlp-main.740", pages = "9369--9390", abstract = "Inductive transfer learning has taken the entire NLP field by storm, with models such as BERT and BART setting new state of the art on countless NLU tasks. However, most of the available models and research have been conducted for English. In this work, we introduce BARThez, the first large-scale pretrained seq2seq model for French. Being based on BART, BARThez is particularly well-suited for generative tasks. We evaluate BARThez on five discriminative tasks from the FLUE benchmark and two generative tasks from a novel summarization dataset, OrangeSum, that we created for this research. We show BARThez to be very competitive with state-of-the-art BERT-based French language models such as CamemBERT and FlauBERT. We also continue the pretraining of a multilingual BART on BARThez{'} corpus, and show our resulting model, mBARThez, to significantly boost BARThez{'} generative performance.", }有排行榜吗?
否
否
覆盖的语言法语
授权许可其他:其他许可证
主要任务摘要生成
否
否
其他细分?否
使用序列到序列模型进行生成式摘要的论文:
(预训练)转换器的论文:
数据卡片中没有独特的技术术语。
该模型生成给定新闻文章的类人标题和摘要的能力。
度量标准ROUGE ,BERT-Score
提出的评估方法自动评估:使用 Rouge-1、Rouge-2、RougeL 和 BERTScore 进行评估。
人工评估:与 11 位法语母语人士进行了一项评估研究。评估人员是作者所在大学的计算机科学系的博士生,从事自然语言处理和其他人工智能领域的工作。他们在收到邮件通知后自愿参加。使用了最佳-最差比例 (Louviere 等人,2015)。将来自两个不同系统的摘要及其输入文档呈现给人工注释员,其任务是决定哪个摘要更好。评估人员被要求根据准确性(摘要是否包含准确的事实?)、信息量(是否捕捉到重要信息?)和流畅度(摘要是否以良好的法语写成?)做出判断。
以前的结果可用吗?否
否
否
否
语言制作者是否代表该语言?数据集包含由专业作者撰写的新闻文章。
开放许可 - 允许商业使用
语言数据的版权限制开放许可 - 允许商业使用