  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.
