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Sequence generative models with RNN variants, such as LSTM, GRU, showpromising performance on abstractive document summarization. However, theystill have some issues that limit their performance, especially while deal-ingwith long sequences. One of the issues is that, to the best of our knowledge,all current models employ a unidirectional decoder, which reasons only aboutthe 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 onlycapture partial aspects of attentional regularities due to the inheritedchallenges in document summarization. To this end, we propose an end-to-endtrainable bidirectional RNN model to tackle the aforementioned issues. Themodel has a bidirectional encoder-decoder architecture; in which the encoderand the decoder are bidirectional LSTMs. The forward decoder is initializedwith the last hidden state of the backward encoder while the backward decoderis initialized with the last hidden state of the for-ward encoder. In addition,a bidirectional beam search mechanism is proposed as an approximate inferencealgo-rithm for generating the output summaries from the bidi-rectional model.This enables the model to reason about the past and future and to generatebalanced outputs as a result. Experimental results on CNN / Daily Mail datasetshow that the proposed model outperforms the current abstractivestate-of-the-art models by a considerable mar-gin.

Bidirectional Attentional Encoder-Decoder Model and Bidirectional Beam Search for Abstractive Summarization