Effective attention-based neural architectures for sentence compression with bidirectional long short-term memory
Nhi-Thao Tran, Viet-Thang Luong, Ngan Luu-Thuy Nguyen, Minh-Quoc Nghiem · 2016
We propose a novel model that apply an extension of the Long Short-Term Memory neural network for sentence compression task. In our model, only the most relevant context of each word is concentrated to avoid the redundant information. Our model is based on two new models that have been successfully used recently in neural machine translation. The first is Bidirectional model that can be trained using all the available input information in the past and future. The second is Attention model that focus not only the whole sentence information but also the particular context of each word in this sentence. Experimental results show that our model significantly outperforms all the recently state-of-the-art method, the Bidirectional and the Attention model on the Google sentence compression dataset.