Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks
Yau-Shian Wang, Hung-yi Lee · 2018
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.