We present a multi-task learning framework for cross-lingual abstractive
summarization to augment training data. Recent studies constructed pseudo
cross-lingual abstractive summarization data to train their neural
encoder-decoders. Meanwhile, we introduce existing genuine data such as
translation pairs and monolingual abstractive summarization data into training.
Our proposed method, Transum, attaches a special token to the beginning of the
input sentence to indicate the target task. The special token enables us to
incorporate the genuine data into the training data easily. The experimental
results show that Transum achieves better performance than the model trained
with only pseudo cross-lingual summarization data. In addition, we achieve the
top ROUGE score on Chinese-English and Arabic-English abstractive
summarization. Moreover, Transum also has a positive effect on machine
translation. Experimental results indicate that Transum improves the
performance from the strong baseline, Transformer, in Chinese-English,
Arabic-English, and English-Japanese translation datasets.

Transum 是一种多任务学习框架，可以将真实数据及伪造数据结合起来用于神经编码器 - 解码器的训练，达到在汉英和阿拉伯英文摘要、中英、阿拉伯英翻译任务中均获得最佳 ROUGE 分的效果，此外还对机器翻译任务有积极的影响。