| Challenge: | LR-Sum contains human-written summaries for 40 languages, many of which are less-resourced. |
| Approach: | They propose to use a permissively-licensed dataset to analyze human-written summaries for 40 languages. |
| Outcome: | The proposed dataset contains human-written summaries for 40 languages . authors describe abstractive and extractive summarization experiments . |
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| Challenge: | Existing summarization datasets focus on overly exposed domains and are primarily monolingual with few multilingual datasets. |
| Approach: | They propose a new summarization dataset based on manually curated document summaries from the European Union law platform EUR-Lex. |
| Outcome: | The proposed dataset is based on document summaries of legal acts from the European Union law platform (EUR-Lex). |
XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages (2021.findings-acl)
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Tahmid Hasan, Abhik Bhattacharjee, Md. Saiful Islam, Kazi Mubasshir, Yuan-Fang Li, Yong-Bin Kang, M. Sohel Rahman, Rifat Shahriyar
| Challenge: | XL-Sum dataset covers 44 languages ranging from low to high-resource . Xl-SUM is highly abstractive, concise, and of high quality . |
| Approach: | They present a dataset comprising 1 million professionally annotated article-summary pairs from BBC . they fine-tune a pretrained multilingual model with XL-Sum and experiment on multilingual and lowresource tasks. |
| Outcome: | The proposed dataset is highly abstractive, concise, and of high quality . it shows higher scores on 10 languages than similar datasets compared to monolingual ones . |
The State and Fate of Summarization Datasets: A Survey (2025.naacl-long)
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| Challenge: | Summarization is the task of shortening a text while preserving the most important information it contains. |
| Approach: | They propose a novel ontology covering sample properties, collection methods and distribution covering sample characteristics, collection method and distribution. |
| Outcome: | The proposed ontology covers sample properties, collection methods and distribution, and can be used to streamline future research into a more coherent body of work. |
MassiveSumm: a very large-scale, very multilingual, news summarisation dataset (2021.emnlp-main)
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| Challenge: | Current research in automatic summarisation is expensive to create, posing a challenge for any language. |
| Approach: | They propose to use a large-scale multilingual summarisation dataset with articles in 92 languages and more than 35 writing scripts to generate a multilingual dataset. |
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Leveraging Digitized Newspapers to Collect Summarization Data in Low-Resource Languages (2026.findings-eacl)
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| Challenge: | Recent studies suggest that summarization in English may be solved, or even "dead" However, there are no accessible, high-quality summarizing datasets in under-represented languages. |
| Approach: | They propose a method for collecting naturally occurring summaries via front-page teasers, where editors summarize full length articles. |
| Outcome: | The proposed method is suited to varying linguistic resources and is available in seven languages. |
DaNewsroom: A Large-scale Danish Summarisation Dataset (2020.lrec-1)
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| Challenge: | Existing datasets for automatic summarisation are English-oriented . however, only very limited datasets exist in languages other than English . |
| Approach: | They present the first large-scale non-English dataset specifically curated for automatic summarisation. |
| Outcome: | The proposed dataset is the first for the Danish language and is compared with existing datasets. |
A Survey on Cross-Lingual Summarization (2022.tacl-1)
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| Challenge: | Cross-lingual summarization is a task of generating a summary in one language for a given document in a different language. |
| Approach: | They present a systematic review of the literature on cross-lingual summarization . they summarize previous efforts and compare them with each other . |
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Summarization Beyond News: The Automatically Acquired Fandom Corpora (2020.lrec-1)
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| Challenge: | Abstractive summarization methods require large corpora to train neural architectures. |
| Approach: | They propose a novel automatic corpus construction approach that automatically constructs large open-licensed summarization corpora from existing large text collections and an evaluation process with human annotators. |
| Outcome: | The proposed approach can be used to train abstractive summarization models on large corpora and through a manual evaluation with human annotators. |
A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches (2025.findings-naacl)
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| Challenge: | Existing approaches for low-resource text summarization use large language models (LLMs) but such models suffer from inconsistent outputs and are difficult to adapt to domain-specific data. |
| Approach: | They propose two methods to effectively utilize large language models for low-resource text summarization. |
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SumSurvey: An Abstractive Dataset of Scientific Survey Papers for Long Document Summarization (2024.findings-acl)
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| Challenge: | a growing need for long document summarization datasets with 16k input is causing problems. |
| Approach: | They propose to use a dataset to analyze salient information in long document summarizations. |
| Outcome: | The proposed dataset outperforms existing models and LLMs in the distribution form of salient information and the distribution of salinal information is an indicator of quality. |