  In this work we present an unsupervised approach to summarize sentences in
abstractive way using Variational Autoencoder (VAE). VAE are known to learn a
semantically rich latent variable, representing high dimensional input. VAEs
are trained by learning to reconstruct the input from the probabilistic latent
variable. Explicitly providing the information about output length during
training influences the VAE to not encode this information and thus can be
manipulated during inference. Instructing the decoder to produce a shorter
output sequence leads to expressing the input sentence with fewer words. We
show on different summarization data sets, that these shorter sentences can not
beat a simple baseline but yield higher ROUGE scores than trying to reconstruct
the whole sentence.
