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

Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation