Extractive text summarization has been an extensive research problem in the
field of natural language understanding. While the conventional approaches rely
mostly on manually compiled features to generate the summary, few attempts have
been made in developing data-driven systems for extractive summarization. To
this end, we present a fully data-driven end-to-end deep network which we call
as Hybrid MemNet for single document summarization task. The network learns the
continuous unified representation of a document before generating its summary.
It jointly captures local and global sentential information along with the
notion of summary worthy sentences. Experimental results on two different
corpora confirm that our model shows significant performance gains compared
with the state-of-the-art baselines.

本研究提出了一种称为混合记忆网络的全数据驱动端到端深度网络，用于单个文档摘要任务，其通过联合捕获局部和全局句子信息以及摘要有价值的句子的概念来学习文档的连续统一表示，实验结果表明，与现有最先进的基线相比，该模型展示了显著的性能提升。