Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New
  Benchmark with Improved Annotation
Yulong Chen Huajian Zhang Yijie Zhou Xuefeng Bai Yueguan Wang Ming Zhong Jianhao Yan Yafu Li Judy Li Michael Zhu Yue Zhang
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.