Year: 2026
Type: conference-paper
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 multidocument summarization dataset in Hebrew 1 .
Authors: Noam Dahan, Omer Kidron, Gabriel Stanovsky
Institution: Hebrew University of Jerusalem
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7140075415
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