  Sequence generative models with RNN variants, such as LSTM, GRU, show
promising performance on abstractive document summarization. However, they
still have some issues that limit their performance, especially while deal-ing
with long sequences. One of the issues is that, to the best of our knowledge,
all current models employ a unidirectional decoder, which reasons only about
the past and still limited to retain future context while giving a prediction.
This makes these models suffer on their own by generating unbalanced outputs.
Moreover, unidirec-tional attention-based document summarization can only
capture partial aspects of attentional regularities due to the inherited
challenges in document summarization. To this end, we propose an end-to-end
trainable bidirectional RNN model to tackle the aforementioned issues. The
model has a bidirectional encoder-decoder architecture; in which the encoder
and the decoder are bidirectional LSTMs. The forward decoder is initialized
with the last hidden state of the backward encoder while the backward decoder
is initialized with the last hidden state of the for-ward encoder. In addition,
a bidirectional beam search mechanism is proposed as an approximate inference
algo-rithm for generating the output summaries from the bidi-rectional model.
This enables the model to reason about the past and future and to generate
balanced outputs as a result. Experimental results on CNN / Daily Mail dataset
show that the proposed model outperforms the current abstractive
state-of-the-art models by a considerable mar-gin.
