Generating summary sentences using Adversarially Regularized Autoencoders with conditional context

Hyesoo Kong, Wooju Kim

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Abstractive summarization is challenging problem, especially abstractive summarization based on unsupervised learning because it must generate whole, unique sentences. In the real world, companies use abstractive summarization to understand customer feedbacks. In many cases, this work is done by humans and so is expensive in terms of time and money. Therefore, there is an increasing demand for machine learning-based abstractive summarization systems. However, most previous abstractive summarization studies were of supervised models. In this paper, we proposed novel abstractive summarization methods that can be trained unsupervisedly. One of the proposed methods is based on Adversarially Regularized Autoencoder(ARAE) model, but abstractive summary generation method for each cluster of similar customers’ reviews, is newly proposed. We further proposed Conditional Adversarially Regularized Autoencoder(CARAE) model which is similar to the ARAE model but with the addition of condition nodes so that additional information about the cluster can be used during summarization. We first performed summary experiments based on Korean and additionally performed experiments on English. In the experiments, we set up some comparison models and used ROUGE and BLEU to evaluate our proposed models’ performance. Overall, our proposed models outperformed the comparison models and CARAE model performed better than the ARAE model.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalExpert Systems with Applications
Publication statusPublished - 2019 Sept 15

Bibliographical note

Publisher Copyright:
© 2019 Elsevier Ltd

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence


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