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Recent neural sequence to sequence models have provided feasible solutionsfor abstractive summarization. However, such models are still hard to tacklelong text dependency in the summarization task. A high-quality summarizationsystem usually depends on strong encoder which can refine important informationfrom long input texts so that the decoder can generate salient summaries fromthe encoder's memory. In this paper, we propose an aggregation mechanism basedon the Transformer model to address the challenge of long text representation.Our model can review history information to make encoder hold more memorycapacity. Empirically, we apply our aggregation mechanism to the Transformermodel and experiment on CNN/DailyMail dataset to achieve higher qualitysummaries compared to several strong baseline models on the ROUGE metrics.

Improving Abstractive Text Summarization with History Aggregation