Cross-lingual science journalism generates popular science stories of
scientific articles different from the source language for a non-expert
audience. Hence, a cross-lingual popular summary must contain the salient
content of the input document, and the content should be coherent,
comprehensible, and in a local language for the targeted audience. We improve
these aspects of cross-lingual summary generation by joint training of two
high-level NLP tasks, simplification and cross-lingual summarization. The
former task reduces linguistic complexity, and the latter focuses on
cross-lingual abstractive summarization. We propose a novel multi-task
architecture - SimCSum consisting of one shared encoder and two parallel
decoders jointly learning simplification and cross-lingual summarization. We
empirically investigate the performance of SimCSum by comparing it with several
strong baselines over several evaluation metrics and by human evaluation.
Overall, SimCSum demonstrates statistically significant improvements over the
state-of-the-art on two non-synthetic cross-lingual scientific datasets.
Furthermore, we conduct an in-depth investigation into the linguistic
properties of generated summaries and an error analysis.

该研究提出了一种新的多任务框架 - SimCSum，它通过对两个高级 NLP 任务（简化和跨语言摘要）进行联合训练来提高跨语言科技新闻的翻译质量，并在人工评估中取得了显著的成果。