Large Language Models for the Summarization of Czech Documents: From History to the Present

TL;DR

This paper explores the use of large multilingual language models for Czech document summarization, introducing new datasets and achieving state-of-the-art results, especially for historical texts and low-resource language settings.

Contribution

It demonstrates the effectiveness of LLMs for Czech summarization, introduces a new historical dataset, and provides baseline results to advance research in this underexplored area.

Findings

LLMs achieve state-of-the-art results on SumeCzech.

The translation-based approach improves summarization quality.

New dataset Posel od Čerchova supports historical Czech text summarization.

Abstract

Text summarization is the task of automatically condensing longer texts into shorter, coherent summaries while preserving the original meaning and key information. Although this task has been extensively studied in English and other high-resource languages, Czech summarization, particularly in the context of historical documents, remains underexplored. This is largely due to the inherent linguistic complexity of Czech and the lack of high-quality annotated datasets. In this work, we address this gap by leveraging the capabilities of Large Language Models (LLMs), specifically Mistral and mT5, which have demonstrated strong performance across a wide range of natural language processing tasks and multilingual settings. In addition, we also propose a translation-based approach that first translates Czech texts into English, summarizes them using an English-language model, and then translates the summaries back into Czech. Our study makes the following main contributions: We demonstrate that LLMs achieve new state-of-the-art results on the SumeCzech dataset, a benchmark for modern Czech text summarization, showing the effectiveness of multilingual LLMs even for morphologically rich, medium-resource languages like Czech. We introduce a new dataset, Posel od \v{C}erchova, designed for the summarization of historical Czech texts. This dataset is derived from digitized 19th-century publications and annotated for abstractive summarization. We provide initial baselines using modern LLMs to facilitate further research in this underrepresented area. By combining cutting-edge models with both modern and historical Czech datasets, our work lays the foundation for further progress in Czech summarization and contributes valuable resources for future research in Czech historical document processing and low-resource summarization more broadly.