Large Language Models for Summarizing Czech Historical Documents and Beyond
Abstract
Text summarization is the task of shortening a larger body of text into a concise version while retaining its essential meaning and key information. While summarization has been significantly explored in English and other high-resource languages, Czech text summarization, particularly for historical documents, remains underexplored due to linguistic complexities and a scarcity of annotated datasets. Large language models such as Mistral and mT5 have demonstrated excellent results on many natural language processing tasks and languages. Therefore, we employ these models for Czech summarization, resulting in two key contributions: (1) achieving new state-of-the-art results on the modern Czech summarization dataset SumeCzech using these advanced models, and (2) introducing a novel dataset called Posel od \v{C}erchova for summarization of historical Czech documents with baseline results. Together, these contributions provide a great potential for advancing Czech text summarization and open new avenues for research in Czech historical text processing.
Keywords
Cite
@article{arxiv.2508.10368,
title = {Large Language Models for Summarizing Czech Historical Documents and Beyond},
author = {Václav Tran and Jakub Šmíd and Jiří Martínek and Ladislav Lenc and Pavel Král},
journal= {arXiv preprint arXiv:2508.10368},
year = {2025}
}
Comments
Published in Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2 (ICAART 2025). Official version: https://www.scitepress.org/Link.aspx?doi=10.5220/0013374100003890