  The number of scientific publications nowadays is rapidly increasing, causing
information overload for researchers and making it hard for scholars to keep up
to date with current trends and lines of work. Consequently, recent work on
applying text mining technologies for scholarly publications has investigated
the application of automatic text summarization technologies, including extreme
summarization, for this domain. However, previous work has concentrated only on
monolingual settings, primarily in English. In this paper, we fill this
research gap and present an abstractive cross-lingual summarization dataset for
four different languages in the scholarly domain, which enables us to train and
evaluate models that process English papers and generate summaries in German,
Italian, Chinese and Japanese. We present our new X-SCITLDR dataset for
multilingual summarization and thoroughly benchmark different models based on a
state-of-the-art multilingual pre-trained model, including a two-stage
`summarize and translate' approach and a direct cross-lingual model. We
additionally explore the benefits of intermediate-stage training using English
monolingual summarization and machine translation as intermediate tasks and
analyze performance in zero- and few-shot scenarios.
