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

SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression