Authors:
Václav Tran
1
;
Jakub Šmíd
1
;
2
;
Jiří Martínek
1
;
2
;
Ladislav Lenc
1
;
2
and
Pavel Král
1
;
2
Affiliations:
1
Department of Computer Science and Engineering, University of West Bohemia in Pilsen, Univerzitní, Pilsen, Czech Republic
;
2
NTIS - New Technologies for the Information Society, University of West Bohemia in Pilsen, Univerzitní, Pilsen, Czech Republic
Keyword(s):
Czech Text Summarization, Deep Neural Networks, Mistral, mT5, Posel od ˇCerchova, SumeCzech, Transformer Models.
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been significantly explored in English and other high-resource languages, Czech text summarization, particularly for historical documents, remains underexplored due to linguistic complexities and a scarcity of annotated datasets.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 ˇCerchova 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.
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