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.- Anthology ID:
- D18-1451
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4187–4195
- Language:
- URL:
- https://aclanthology.org/D18-1451
- DOI:
- 10.18653/v1/D18-1451
- Cite (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.
- Cite (Informal):
- Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks (Wang & Lee, EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/naacl24-info/D18-1451.pdf