We explore data augmentation methods and evaluate state-of-the-art neural abstractive models on this new task.
we show that semantic augmentation from question datasets improves the overall performance, and that pointer-generator networks outperform sequence-to-sequence attentional models on this task, with a ROUGE-1 score of 44.16%.