ARTeLab/ilpost

ilpost数据集包含来自IlPost的新闻文章，主要用于摘要生成任务，支持抽象摘要和摘要生成。数据集的语言为意大利语，且为单语言数据集。数据集的规模在10K到100K之间。数据集中包含两个特征：source（输入的新闻文章）和target（文章的摘要）。

原始信息汇总

数据集概述

数据集描述

数据集总结

IlPost数据集包含来自IlPost的新闻文章。数据集包含两个特征：

• source: 输入的新闻文章。
• target: 文章的摘要。

支持的任务和排行榜

• 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

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数据字段

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数据分割

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数据集创建

筛选理由

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源数据

初始数据收集和规范化

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源语言生产者

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注释

注释过程

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注释者

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个人和敏感信息

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使用数据的考虑因素

数据集的社会影响

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偏见的讨论

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其他已知限制

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附加信息

数据集管理者

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许可信息

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引用信息

详细信息和结果见发表的工作

@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} }