  We present two novel and contrasting Recurrent Neural Network (RNN) based
architectures for extractive summarization of documents. The Classifier based
architecture sequentially accepts or rejects each sentence in the original
document order for its membership in the final summary. The Selector
architecture, on the other hand, is free to pick one sentence at a time in any
arbitrary order to piece together the summary. Our models under both
architectures jointly capture the notions of salience and redundancy of
sentences. In addition, these models have the advantage of being very
interpretable, since they allow visualization of their predictions broken up by
abstract features such as information content, salience and redundancy. We show
that our models reach or outperform state-of-the-art supervised models on two
different corpora. We also recommend the conditions under which one
architecture is superior to the other based on experimental evidence.
