| Challenge: | Existing work on multilingual summarization and cross-lingual summmarization has been limited due to their different definitions. |
| Approach: | They propose to unify MLS and CLS into a more general setting, i.e. many-to-many summarization. |
| Outcome: | The proposed model outperforms the state-of-the-art models in the zero-shot directions. |
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| Challenge: | Cross-lingual summarization (CLS) aims at producing a summary in the target language for an article in the source language. |
| Approach: | They propose a mixed-lingual pre-training scheme that leverages both cross-lingual tasks such as translation and monolingual tasks like masked language models. |
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An Empirical Study of Many-to-Many Summarization with Large Language Models (2025.acl-long)
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Jiaan Wang, Fandong Meng, Zengkui Sun, Yunlong Liang, Yuxuan Cao, Jiarong Xu, Haoxiang Shi, Jie Zhou
| Challenge: | Recent studies have shown that large language models (LLMs) have strong multilingual abilities, giving them the potential to perform M2MS in real applications. |
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Models and Datasets for Cross-Lingual Summarisation (2021.emnlp-main)
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| Challenge: | Recent years have witnessed increased interest in abstractive summarisation thanks to the popularity of neural network models and the availability of datasets containing hundreds of thousands of document-summary pairs. |
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NCLS: Neural Cross-Lingual Summarization (D19-1)
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| Challenge: | Existing approaches to cross-lingual summarization divide the task into two steps: summarizing and translation. |
| Approach: | They propose to integrate two related tasks into the training process of CLS under multi-task learning to improve cross-lingual summarization. |
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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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Multilingual Generation in Abstractive Summarization: A Comparative Study (2024.lrec-main)
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| Challenge: | Existing models for multilingual generation lack thorough analysis due to extensive linguistic diversity. |
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Multi-Target Cross-Lingual Summarization: a novel task and a language-neutral approach (2024.findings-emnlp)
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| Challenge: | Existing methods to summarize documents in multiple languages are not systematically evaluated to ensure semantic coherence across target languages. |
| Approach: | They propose a principled re-ranking approach to ensure semantic coherence in documents in multiple target languages while ensuring semantic similarity across target languages. |
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Cross-Lingual Abstractive Summarization with Limited Parallel Resources (2021.acl-long)
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| Challenge: | Existing approaches to cross-lingual summarization use limited available cross-linguistic resources. |
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MLSUM: The Multilingual Summarization Corpus (2020.emnlp-main)
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| Challenge: | Existing biases in multi-lingual datasets are limiting the use of multilingual data in document summarization tasks. |
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SumTra: A Differentiable Pipeline for Few-Shot Cross-Lingual Summarization (2024.naacl-long)
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| Challenge: | Existing approaches to cross-lingual summarization are limited due to limited training data. |
| Approach: | They propose to re-use existing multilingual summarization and translation pipelines to perform cross-lingual summaries in a sequence. |
| Outcome: | The proposed approach outperforms existing methods in many languages with only 10% of the fine-tuning samples. |