  Sentence compression is a Natural Language Processing (NLP) task aimed at
shortening original sentences and preserving their key information. Its
applications can benefit many fields e.g. one can build tools for language
education. However, current methods are largely based on Recurrent Neural
Network (RNN) models which suffer from poor processing speed. To address this
issue, in this paper, we propose a token-wise Convolutional Neural Network, a
CNN-based model along with pre-trained Bidirectional Encoder Representations
from Transformers (BERT) features for deletion-based sentence compression. We
also compare our model with RNN-based models and fine-tuned BERT. Although one
of the RNN-based models outperforms marginally other models given the same
input, our CNN-based model was ten times faster than the RNN-based approach.
