- Danqing Wang, Jiaze Chen, Hao Zhou, Xipeng Qiu, Lei Li. 2021. Contrastive Aligned Joint Learning for Multilingual Summarization. Abstract: Multilingual text summarization requires the ability to understand documents in multiple languages and generate summaries in the corresponding language, which poses more challenges on current summarization systems. However, this problem has been rarely studied due to the lack of large-scale supervised summarization data in multiple languages. In this paper, we ﬁrst provide a large-scale multilingual summarization corpus MLGSum consisting of 1.1 million articles and summaries in 12 different languages. Based on it, we develop a uniﬁed summarization model to understand the document and generate summaries in different languages. We use the contrastive learning strategy to train our multilingual summarization system (CALMS), which consists of two training objectives, contrastive sentence ranking (CSR) and sentence aligned substitution (SAS). The two training objectives are designed to share salient information extractive ability and align sentence-level representation across different languages. Experimental results indicate that CALMS achieves signiﬁcant improvement over mono-lingual models in all languages. We further transfer CALMS to other languages and ﬁnd that it will also beneﬁt similar languages. Our code and dataset are available at https://github.com/brxx122/CALMS.
