  Sentence compression is the task of creating a shorter version of an input
sentence while keeping important information. In this paper, we extend the task
of compression by deletion with the use of contextual embeddings. Different
from prior work usually using non-contextual embeddings (Glove or Word2Vec), we
exploit contextual embeddings that enable our model capturing the context of
inputs. More precisely, we utilize contextual embeddings stacked by
bidirectional Long-short Term Memory and Conditional Random Fields for dealing
with sequence labeling. Experimental results on a benchmark Google dataset show
that by utilizing contextual embeddings, our model achieves a new
state-of-the-art F-score compared to strong methods reported on the leader
board.
