A Survey on Cross-Lingual Summarization

A Survey on Cross-Lingual Summarization Jiaan Wang1∗, Fandong Meng2†, Duo Zheng4, Yunlong Liang2 Zhixu Li3†, Jianfeng Qu1 and Jie Zhou2 1School of Computer Science and Technology, Soochow University, Suzhou, China 2Pattern Recognition Center, WeChat AI, Tencent Inc, China 3Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China 4Beijing University of Posts and Telecommunications, Beijing, China jawang1@stu.suda.edu.cn, {fandongmeng,yunlonliang,withtomzhou}@tencent.com zd@bupt.edu.cn, zhixuli@fudan.edu.cn, jfqu@suda.edu.cn Abstract Cross-lingual summarization is the task of generating a summary in one language (e.g., English) for the given document(s) in a differ- ent language (e.g., Chinese). Under the glob- alization background, this task has attracted increasing attention of the computational lin- guistics community. Nevertheless, there still remains a lack of comprehensive review for this task. Therefore, we present the first systematic critical review on the datasets, ap- proaches, and challenges in this field. Specif- ically, we carefully organize existing datasets and approaches according to different con- struction methods and solution paradigms, respectively. For each type of dataset or ap- proach, we thoroughly introduce and summa- rize previous efforts and further compare them with each other to provide deeper analyses. In the end, we also discuss promising directions and offer our thoughts to facilitate future re- search. This survey is for both beginners and experts in cross-lingual summarization, and we hope it will serve as a starting point as well as a source of new ideas for researchers and engineers interested in this area. 1 Introduction To help people efficiently grasp the gist of documents in a foreign language, Cross-Lingual Summarization (XLS) aims to generate a summary in the target language from the given document(s) in a different source language. This task could be regarded as a combination of monolingual sum- ∗Work was done when Jiaan Wang was interning at Pattern Recognition Center, WeChat AI, Tencent Inc, China. †Corresponding authors. marization (MS) and machine translation (MT), both of which are unsolved natural language pro- cessing (NLP) tasks and have been continuously studied for decades (Paice, 1990; Brown et al., 1993). XLS is an extremely challenging task: (1) from the perspective of data, unlike MS, natu- rally occurring documents in a source language paired with the corresponding summaries in dif- ferent target languages are rare, making it difficult to collect large-scale and human-annotated data- sets (Ladhak et al., 2020; Perez-Beltrachini and Lapata, 2021); (2) from the perspective of models, XLS requires both the abilities to translate and summarize, which makes it hard to generate accu- rate summaries by directly conducting XLS (Cao et al., 2020). Despite its importance, XLS has attracted a little attention (Leuski et al., 2003; Wan et al., 2010) in the statistical learning era due to its dif- ficulties and the scarcity of parallel corpus. Re- cent years have witnessed the rapid development of neural networks, especially the emergence of pre-trained encoder-decoder models (Zhang et al., 2020a; Raffel et al., 2020; Lewis et al., 2020; Liu et al., 2020; Tang et al., 2021; Xue et al., 2021), making neural summarizers and transla- tors achieve …

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