公开韩语新闻摘要数据集，助力中文模型本地化训练。
Naver-News-KO: A Korean News Summarization Dataset for Open-Source Fine-Tuning of Summarization Models
- 从Naver新闻抓取2.7万对文档与摘要，分经济与科技两类
- 提供KoBART和Gemma模型的可复现微调基线，ROUGE-1达56.6
- 适合做韩语摘要、多语言生成或开源模型训练的研究者
我们发布Naver-News-KO，一个包含27,400对（文档, 摘要）的韩语新闻摘要数据集，采自2022年7月十天内Naver News的经济与IT/科学类内容（占比77:23），训练/验证/测试集分别为22,194 / 2,466 / 2,740。平均文档到摘要字符压缩比为6.03倍。该数据集自2023年1月起在Hugging Face公开，截至2026年5月每月下载量约3.3万次。社区已基于此训练出Gemma-2B-ko等模型。本文报告了数据收集流程、字段结构、划分方式，统计了长度分布、压缩率，并测得测试集与训练集标题近似重复率达16.8%。提供了Lead-3提取基线（ROUGE-1 55.1，ROUGE-L 50.6）及两个可复现微调基线：KoBART（R-1 56.6，BERTScore-F1 81.5）和使用LoRA的Gemma-2B-ko（R-1 55.3，BERTScore-F1 78.3），附带训练脚本。强调数据许可与使用范围，旨在为已使用该数据集的研究提供可引用参考，非提出新基准。
原文摘要 · Abstract (English)
We release Naver-News-KO, a Korean news summarization dataset of 27,400 (document, summary) pairs collected from Naver News over a ten-day window in July 2022 across two categories (Economy and IT/Science; 77/23 split), with train/validation/test partitions of 22,194 / 2,466 / 2,740 and a mean per-record document-to-summary character-compression ratio of 6.03x. The dataset has been publicly hosted on the Hugging Face Hub since January 2023 and, as of May 2026, receives approximately 33,000 downloads per month; community-maintained Korean summarization models fine-tuned on it include Gemma-2B-ko and Gemma2-9B variants. This technical report (i) documents the collection protocol, the column schema, and the split construction, (ii) reports corpus-level statistics (length distributions, compression ratio, and a measured 16.8% near-duplicate title-Jaccard overlap between test and train that users should be aware of), (iii) positions the resource against other open Korean summarization corpora, (iv) provides a Lead-3 extractive reference point (ROUGE-1 55.1, ROUGE-L 50.6) and two reproducible fine-tuned baselines -- KoBART (R-1 56.6, BERTScore-F1 81.5) and Gemma-2B-ko with LoRA (R-1 55.3, BERTScore-F1 78.3) -- with release-time training scripts, and (v) clarifies the licensing and intended-use scope of the resource. The goal is to provide a citable reference for downstream work that already uses this dataset, not to propose a new benchmark.
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