https://scholars.lib.ntu.edu.tw/handle/123456789/558975
Title: | Learning to encode text as human-readable summaries using generative adversarial networks | Authors: | Wang, Y.-S. HUNG-YI LEE |
Issue Date: | 2020 | Start page/Pages: | 4187-4195 | Source: | Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 | 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. © 2018 Association for Computational Linguistics |
URI: | https://www.scopus.com/inward/record.url?eid=2-s2.0-85070676881&partnerID=40&md5=2ff5060522271fe88ed14998f67e440c https://scholars.lib.ntu.edu.tw/handle/123456789/558975 |
SDG/Keyword: | Encoding (symbols); Learning systems; Network coding; Adversarial networks; Auto encoders; Chinese corpus; Human-readable; Input datas; Training data; Natural language processing systems |
Appears in Collections: | 電機工程學系 |
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