Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks

Yau-Shian Wang; Hung-Yi Lee

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arXiv · 1810.02851

Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks

Yau-Shian Wang·Hung-Yi Lee

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Abstract

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.

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BibTeX RIS

Yau-Shian Wang, Hung-Yi Lee. 2018-10-05. Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks. https://arxiv.org/abs/1810.02851

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