数据集:

ARTeLab/fanpage

语言:

it

计算机处理:

monolingual

大小:

10K<n<100K

源数据集:

original
英文

fanpage 数据集卡片

数据集摘要

fanpage 数据集,包含来自Fanpage的新闻文章。

有两个特征:

  • source:输入的新闻文章。
  • target:文章的摘要。

支持的任务和排名

  • abstractive-summarization,summarization

语言

数据集中的文本为意大利语。

数据集结构

数据实例

[需要更多信息]

数据字段

[需要更多信息]

数据拆分

[需要更多信息]

数据集创建

策划理由

[需要更多信息]

来源数据

初始数据收集和规范化

[需要更多信息]

谁是源语言的生产者?

[需要更多信息]

注释

注释过程

[需要更多信息]

谁是注释者?

[需要更多信息]

个人和敏感信息

[需要更多信息]

使用数据的注意事项

数据的社会影响

[需要更多信息]

偏见讨论

[需要更多信息]

其他已知限制

[需要更多信息]

其他信息

数据集策划者

[需要更多信息]

许可信息

[需要更多信息]

引用信息

更多细节和结果请参考 published work

@Article{info13050228,
    AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo},
    TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization},
    JOURNAL = {Information},
    VOLUME = {13},
    YEAR = {2022},
    NUMBER = {5},
    ARTICLE-NUMBER = {228},
    URL = {https://www.mdpi.com/2078-2489/13/5/228},
    ISSN = {2078-2489},
    ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.},
    DOI = {10.3390/info13050228}
}