  Neural sequence-to-sequence models are currently the dominant approach in
several natural language processing tasks, but require large parallel corpora.
We present a sequence-to-sequence-to-sequence autoencoder (SEQ^3), consisting
of two chained encoder-decoder pairs, with words used as a sequence of discrete
latent variables. We apply the proposed model to unsupervised abstractive
sentence compression, where the first and last sequences are the input and
reconstructed sentences, respectively, while the middle sequence is the
compressed sentence. Constraining the length of the latent word sequences
forces the model to distill important information from the input. A pretrained
language model, acting as a prior over the latent sequences, encourages the
compressed sentences to be human-readable. Continuous relaxations enable us to
sample from categorical distributions, allowing gradient-based optimization,
unlike alternatives that rely on reinforcement learning. The proposed model
does not require parallel text-summary pairs, achieving promising results in
unsupervised sentence compression on benchmark datasets.
