  Auto-encoders compress input data into a latent-space representation and
reconstruct the original data from the representation. This latent
representation is not easily interpreted by humans. In this paper, we propose
training an auto-encoder that encodes input text into human-readable sentences,
and unpaired abstractive summarization is thereby achieved. The auto-encoder is
composed of a generator and a reconstructor. The generator encodes the input
text into a shorter word sequence, and the reconstructor recovers the generator
input from the generator output. To make the generator output human-readable, a
discriminator restricts the output of the generator to resemble human-written
sentences. By taking the generator output as the summary of the input text,
abstractive summarization is achieved without document-summary pairs as
training data. Promising results are shown on both English and Chinese corpora.
