  The recent advance in neural network architecture and training algorithms
have shown the effectiveness of representation learning. The neural
network-based models generate better representation than the traditional ones.
They have the ability to automatically learn the distributed representation for
sentences and documents. To this end, we proposed a novel model that addresses
several issues that are not adequately modeled by the previously proposed
models, such as the memory problem and incorporating the knowledge of document
structure. Our model uses a hierarchical structured self-attention mechanism to
create the sentence and document embeddings. This architecture mirrors the
hierarchical structure of the document and in turn enables us to obtain better
feature representation. The attention mechanism provides extra source of
information to guide the summary extraction. The new model treated the
summarization task as a classification problem in which the model computes the
respective probabilities of sentence-summary membership. The model predictions
are broken up by several features such as information content, salience,
novelty and positional representation. The proposed model was evaluated on two
well-known datasets, the CNN / Daily Mail, and DUC 2002. The experimental
results show that our model outperforms the current extractive state-of-the-art
by a considerable margin.
