Recent work has suggested that end-to-end system designs for cross-lingual summarization are competitive solutions that perform on par or even better than traditional pipelined designs. A closer look at the evidence reveals that this intuition is based on the results of only a handful of languages or using underpowered pipeline baselines. In this work, we compare these two paradigms for cross-lingual summarization on 39 source languages into English and show that a simple \textit{translate-then-summarize} pipeline design consistently outperforms even an end-to-end system with access to enormous amounts of parallel data. For languages where our pipeline model does not perform well, we show that system performance is highly correlated with publicly distributed BLEU scores, allowing practitioners to establish the feasibility of a language pair a priori. Contrary to recent publication trends, our result suggests that the combination of individual progress of monolingual summarization and translation tasks offers better performance than an end-to-end system, suggesting that end-to-end designs should be considered with care.

本研究探讨了跨语言摘要中端到端系统设计与传统管道设计之间的差异，发现基于39种源语言的比较显示“翻译后总结”管道设计始终优于端到端系统。研究表明，单语摘要和翻译任务的进展结合能够提供比端到端系统更好的性能，这一结果倡导在设计时谨慎考虑端到端系统。 

优秀机器翻译无需端到端：翻译后总结的跨语言摘要方法