Most existing cross-lingual summarization (CLS) work constructs CLS corpora by simply and directly translating pre-annotated summaries from one language to another, which can contain errors from both summarization and translation processes. To address this issue, we propose ConvSumX, a cross-lingual conversation summarization benchmark, through a new annotation schema that explicitly considers source input context. ConvSumX consists of 2 sub-tasks under different real-world scenarios, with each covering 3 language directions. We conduct thorough analysis on ConvSumX and 3 widely-used manually annotated CLS corpora and empirically find that ConvSumX is more faithful towards input text. Additionally, based on the same intuition, we propose a 2-Step method, which takes both conversation and summary as input to simulate human annotation process. Experimental results show that 2-Step method surpasses strong baselines on ConvSumX under both automatic and human evaluation. Analysis shows that both source input text and summary are crucial for modeling cross-lingual summaries.

翻译：大多数现有的跨语言摘要（CLS）工作通过简单直接地将预标注摘要从一种语言翻译成另一种语言来构建CLS语料库，这一过程可能包含来自摘要生成和翻译两个步骤的错误。为解决此问题，我们提出ConvSumX——一个跨语言对话摘要基准，通过一种明确考虑源输入上下文的新的标注框架构建。该基准包含两个子任务，分别对应不同的真实场景，每个子任务涵盖三种语言方向。我们对ConvSumX及三个广泛使用的人工标注CLS语料库进行了深入分析，实证结果表明ConvSumX对源文本具有更高的忠实度。此外，基于相同的直觉，我们提出了一种两步法（2-Step方法），该方法将对话和摘要同时作为输入，以模拟人工标注过程。实验结果显示，在自动评估和人工评估两种方式下，两步法在ConvSumX上均超越了强基线模型。分析表明，源输入文本和摘要对跨语言摘要的建模均至关重要。