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        <title>Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks</title>
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        <namePart type="given">Yaushian</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
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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.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1451</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks
%A Wang, Yaushian
%A Lee, Hung-Yi
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F wang-lee-2018-learning
%X 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.
%R 10.18653/v1/D18-1451
%U https://aclanthology.org/D18-1451
%U https://doi.org/10.18653/v1/D18-1451
%P 4187-4195

Markdown (Informal)

[Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks](https://aclanthology.org/D18-1451) (Wang & Lee, EMNLP 2018)

• Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks (Wang & Lee, EMNLP 2018)

ACL

• Yaushian Wang and Hung-Yi Lee. 2018. Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4187–4195, Brussels, Belgium. Association for Computational Linguistics.