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.

现有的跨语言摘要（CLS）工作往往通过直接翻译预注摘要来构建 CLS 语料库，可能会包含摘要和翻译过程中的错误。为了解决这个问题，我们提出了 ConvSumX，这是一个跨语言对话摘要基准，通过一个新的注释模式明确考虑源输入上下文。ConvSumX 包括两个子任务，涵盖了三个不同的语言方向。我们对 ConvSumX 和三个广泛使用的手动注释的 CLS 语料库进行了详尽的分析，实验证明 ConvSumX 更加忠实于输入文本。此外，基于相同的观点，我们提出了一个 2 步方法，该方法将对话和摘要作为输入以模拟人工注释过程。实验结果表明，2 步方法在 ConvSumX 上超过了强基线模型，并在自动和人工评估下显示出优势。分析表明，源输入文本和摘要对于建模跨语言摘要至关重要。