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

ClueGraphSum: Let Key Clues Guide the Cross-Lingual Abstractive Summarization