| 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. |
| Outcome: | The proposed framework improves on English-to-Chinese and Chinese-to English CLS human-corrected test sets. |
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Mixed-Lingual Pre-training for Cross-lingual Summarization (2020.aacl-main)
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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. |
| Outcome: | The proposed model improves on the translation and masked language models with no task-specific components and saves memory. |
Attend, Translate and Summarize: An Efficient Method for Neural Cross-Lingual Summarization (2020.acl-main)
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| Challenge: | Existing methods for cross-lingual summarization are pipeline-based, but they suffer from error propagation. |
| Approach: | They propose a method that attends to some words in the source text, then translates them into the target language to get the final summary. |
| Outcome: | The proposed method outperforms baseline methods on Chinese-to-English and English-to Chinese summarization tasks. |
A Variational Hierarchical Model for Neural Cross-Lingual Summarization (2022.acl-long)
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| Challenge: | Existing studies on cross-lingual summarization focus on pipeline methods or jointly training an end-to-end model through an auxiliary MT or MS objective. |
| Approach: | They propose a hierarchical model for the cross-lingual summarization task . the model is based on the conditional variational auto-encoder . |
| Outcome: | The proposed model generates better cross-lingual summaries than comparison models in the few-shot setting. |
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. |
| Approach: | They propose to create a cross-lingual summarisation corpus with long documents in a source language associated with multi-sentence summaries in . target language. |
| Outcome: | The proposed task can be applied to several other languages and covers twelve languages and directions. |
Jointly Learning to Align and Summarize for Neural Cross-Lingual Summarization (2020.acl-main)
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| Challenge: | Existing studies on cross-lingual summarization focus on pipeline methods and training end-to-end models. |
| Approach: | They propose to jointly learn to align and align to train a neural cross-lingual summarization model by using a large-scale corpus. |
| Outcome: | The proposed model outperforms competing models in most cases and can generate cross-lingual summaries without access to any cross-linguistic corpus. |
Towards Unifying Multi-Lingual and Cross-Lingual Summarization (2023.acl-long)
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| 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. |
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 . |
| Outcome: | The proposed approach is compared with previous approaches and summarizes them to provide a deeper analysis. |
Understanding Translationese in Cross-Lingual Summarization (2023.findings-emnlp)
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| Challenge: | Existing datasets involve translation, but translationese is distinguished from original text . previous studies have shown that translationeses in CLS are not a problem in training sets . |
| Approach: | They propose to use cross-lingual summarization to generate a concise summary in a target language from a document in . existing datasets typically involve translation in their creation, but the translated text is distinguished from the original written in that language. |
| Outcome: | The proposed method systematically investigates how translationese affects CLS model evaluation and performance when it appears in source documents or target summaries. |
Multi-Task Learning for Cross-Lingual Abstractive Summarization (2022.lrec-1)
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| Challenge: | Existing studies use pseudo cross-lingual abstractive summarization data to train neural encoder-decoders. |
| Approach: | They propose a multi-task learning framework for cross-lingual abstractive summarization that attaches a special token to the beginning of the input sentence to indicate the target task. |
| Outcome: | The proposed model achieves better performance than the model trained with only pseudo cross-lingual abstractive summarization data. |
Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation (2023.acl-long)
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Yulong Chen, Huajian Zhang, Yijie Zhou, Xuefeng Bai, Yueguan Wang, Ming Zhong, Jianhao Yan, Yafu Li, Judy Li, Xianchao Zhu, Yue Zhang
| Challenge: | Existing work on cross-lingual summarization (CLS) does not consider crosslingual sources for summarizing. |
| Approach: | They propose a cross-lingual conversation summarization benchmark that explicitly considers source context. |
| Outcome: | The proposed method surpasses baselines on ConvSumX and 3 widely-used manual annotations. |