Naver-News-KO: A Korean News Summarization Dataset for Open-Source Fine-Tuning of Summarization Models
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Computer Science > Computation and Language
arXiv:2607.20442 (cs)
[Submitted on 12 May 2026]
Title:Naver-News-KO: A Korean News Summarization Dataset for Open-Source Fine-Tuning of Summarization Models
Authors:Daekeun Kim
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Abstract: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.
| Comments: | Technical report; 7 pages, 1 figure, 4 tables. Dataset: this https URL |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.20442 [cs.CL] |
| (or arXiv:2607.20442v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.20442
arXiv-issued DOI via DataCite
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