Parallel cross-lingual summarization data is scarce, requiring models to better use the limited available cross-lingual resources. Existing methods to do so often adopt sequence-to-sequence networks with multi-task frameworks. Such approaches apply multiple decoders, each of which is utilized for a specific task. However, these independent decoders share no parameters, hence fail to capture the relationships between the discrete phrases of summaries in different languages, breaking the connections in order to transfer the knowledge of the high-resource languages to low-resource languages. To bridge these connections, we propose a novel Multi-Task framework for Cross-Lingual Abstractive Summarization (MCLAS) in a low-resource setting. Employing one unified decoder to generate the sequential concatenation of monolingual and cross-lingual summaries, MCLAS makes the monolingual summarization task a prerequisite of the CLS task. In this way, the shared decoder learns interactions involving alignments and summary patterns across languages, which encourages attaining knowledge transfer. Experiments on two CLS datasets demonstrate that our model significantly outperforms three baseline models in both low-resource and full-dataset scenarios. Moreover, in-depth analysis on the generated summaries and attention heads verifies that interactions are learned well using MCLAS, which benefits the CLS task under limited parallel resources.

翻译：平行的跨语言总和数据稀少,需要模型来更好地利用有限的现有跨语言资源。现有的方法往往采用多任务框架的顺序和顺序网络。这些方法采用多个解码器,每个解码器都用于具体任务。然而,这些独立的解码器没有共享参数,因此无法捕捉不同语言摘要中不同词句之间的关系,从而断开连接,将高资源语言知识传授给低资源语言。为了连接这些连接,我们提议了一个创新的跨语言摘要拼凑多任务框架(MCLAS),在低资源环境中,我们提议了一个新的跨语言摘要拼凑多任务框架(MCLAS)。使用一个统一的解码器来产生单语和跨语言摘要的顺序组合。但是,这些独立解码器将单语拼凑任务作为CLS任务的前提。 以这种方式,共享解码器学习了跨语言的校对和摘要模式的相互作用,从而鼓励实现知识转让。两个CLS数据集的实验表明,我们的模型大大超越了在低资源深度分析中产生的CLIS基准模型和全面分析中产生的CLS。