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        <title>Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languages</title>
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        <namePart type="given">Noam</namePart>
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    <abstract>High quality summarization data remains scarce in under-represented languages. However, historical newspapers, made available through recent digitization efforts, offer an abundant source of untapped, naturally annotated data. In this work, we present a novel method for collecting naturally occurring summaries via Front-Page Teasers, where editors summarize full length articles. We show that this phenomenon is common across seven diverse languages and supports multi-document summarization. To scale data collection, we develop an automatic process, suited to varying linguistic resource levels. Finally, we apply this process to a Hebrew newspaper title, producing HEBTEASESUM, the first dedicated multi-document summarization dataset in Hebrew.</abstract>
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%0 Conference Proceedings
%T Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languages
%A Dahan, Noam
%A Kidron, Omer
%A Stanovsky, Gabriel
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F dahan-etal-2026-leveraging
%X High quality summarization data remains scarce in under-represented languages. However, historical newspapers, made available through recent digitization efforts, offer an abundant source of untapped, naturally annotated data. In this work, we present a novel method for collecting naturally occurring summaries via Front-Page Teasers, where editors summarize full length articles. We show that this phenomenon is common across seven diverse languages and supports multi-document summarization. To scale data collection, we develop an automatic process, suited to varying linguistic resource levels. Finally, we apply this process to a Hebrew newspaper title, producing HEBTEASESUM, the first dedicated multi-document summarization dataset in Hebrew.
%U https://aclanthology.org/2026.findings-eacl.278/
%P 5260-5273

Markdown (Informal)

[Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languages](https://aclanthology.org/2026.findings-eacl.278/) (Dahan et al., Findings 2026)

• Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languages (Dahan et al., Findings 2026)

ACL

• Noam Dahan, Omer Kidron, and Gabriel Stanovsky. 2026. Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languages. In Findings of the Association for Computational Linguistics: EACL 2026, pages 5260–5273, Rabat, Morocco. Association for Computational Linguistics.