mlsanityData to 14 Oct 2026

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

arXiv 2511.14598 ↗First posted 18 Nov 2025cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 47 references that are in the map. First posted on arXiv 18 Nov 2025.

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21 of 47 references.

References · 21

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  3. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,818
  4. 2018Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationcited by 1,317
  5. 2015Teaching Machines to Read and Comprehendcited by 1,049
  6. 2020The State and Fate of Linguistic Diversity and Inclusion in the NLP Worldcited by 798
  7. 2020SummEval: Re-evaluating Summarization Evaluationcited by 674
  8. 2023Benchmarking Large Language Models for News Summarizationcited by 445
  9. 2023Is ChatGPT a Good NLG Evaluator? A Preliminary Studycited by 422
  10. 2018Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategiescited by 294
  11. 2022Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluationcited by 142
  12. 2016Neural Summarization by Extracting Sentences and Wordscited by 116
9 more
  1. 2020A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portalcited by 72
  2. 2020From SPMRL to NMRL: What Did We Learn (and Unlearn) in a Decade of Parsing Morphologically-Rich Languages (MRLs)?cited by 26
  3. 2022How can NLP Help Revitalize Endangered Languages? A Case Study and Roadmap for the Cherokee Languagecited by 19
  4. 2021How well do you know your summarization datasets?cited by 16
  5. 2024HeSum: a Novel Dataset for Abstractive Text Summarization in Hebrewcited by 6
  6. 2023Measuring Intersectional Biases in Historical Documentscited by 4
  7. 2024How Much Annotation is Needed to Compare Summarization Models?cited by 3
  8. 2024The State and Fate of Summarization Datasets: A Surveycited by 3
  9. 2024A Mixed-Language Multi-Document News Summarization Dataset and a Graphs-Based Extract-Generate Modelcited by 2

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