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Various Seq2Seq learning models designed for machine translation were appliedfor abstractive summarization task recently. Despite these models provide highROUGE scores, they are limited to generate comprehensive summaries with a highlevel of abstraction due to its degenerated attention distribution. Weintroduce Diverse Convolutional Seq2Seq Model(DivCNN Seq2Seq) usingDeterminantal Point Processes methods(Micro DPPs and Macro DPPs) to produceattention distribution considering both quality and diversity. Without breakingthe end to end architecture, DivCNN Seq2Seq achieves a higher level ofcomprehensiveness compared to vanilla models and strong baselines. All thereproducible codes and datasets are available online.

In Conclusion Not Repetition: Comprehensive Abstractive Summarization With Diversified Attention Based On Determinantal Point Processes