Learning to encode text as human-readable summaries using generative adversarial networks

Wang, Y.-S.Y.-S.WangHUNG-YI LEE2021-05-052021-05-052020https://www.scopus.com/inward/record.url?eid=2-s2.0-85070676881&partnerID=40&md5=2ff5060522271fe88ed14998f67e440chttps://scholars.lib.ntu.edu.tw/handle/123456789/558975Auto-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 LinguisticsEncoding (symbols); Learning systems; Network coding; Adversarial networks; Auto encoders; Chinese corpus; Human-readable; Input datas; Training data; Natural language processing systemsLearning to encode text as human-readable summaries using generative adversarial networksconference paper2-s2.0-85070676881