  Cross-Lingual Summarization (CLS) is the task to generate a summary in one
language for an article in a different language. Previous studies on CLS mainly
take pipeline methods or train the end-to-end model using the translated
parallel data. However, the quality of generated cross-lingual summaries needs
more further efforts to improve, and the model performance has never been
evaluated on the hand-written CLS dataset. Therefore, we first propose a
clue-guided cross-lingual abstractive summarization method to improve the
quality of cross-lingual summaries, and then construct a novel hand-written CLS
dataset for evaluation. Specifically, we extract keywords, named entities, etc.
of the input article as key clues for summarization and then design a
clue-guided algorithm to transform an article into a graph with less noisy
sentences. One Graph encoder is built to learn sentence semantics and article
structures and one Clue encoder is built to encode and translate key clues,
ensuring the information of important parts are reserved in the generated
summary. These two encoders are connected by one decoder to directly learn
cross-lingual semantics. Experimental results show that our method has stronger
robustness for longer inputs and substantially improves the performance over
the strong baseline, achieving an improvement of 8.55 ROUGE-1
(English-to-Chinese summarization) and 2.13 MoverScore (Chinese-to-English
summarization) scores over the existing SOTA.
