Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languages

Noam Dahan, Omer Kidron, Gabriel Stanovsky
11/18/2025
cs.CL

No official code found. Best implementation: edahanoam/HebTeaseSum (1 star, last commit 2026-03-25).

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.

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Code Implementations(1)

edahanoam/HebTeaseSumCommunity67%

Hebrew summarization dataset based on our paper "Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languages"

11PythonAug 28, 20257 months ago

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Cite this paper

@article{dahan2025leveraging,
  title  = {Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languages},
  author = {Noam Dahan and Omer Kidron and Gabriel Stanovsky},
  year   = {2025},
  eprint = {2511.14598},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2511.14598v1}
}

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