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Generating summary sentences using Adversarially Regularized Autoencoders with conditional context
- Source :
- Expert Systems with Applications. 130:1-11
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- 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.
- Subjects :
- 0209 industrial biotechnology
Computer science
business.industry
General Engineering
Context (language use)
02 engineering and technology
Machine learning
computer.software_genre
Autoencoder
Automatic summarization
Computer Science Applications
Set (abstract data type)
020901 industrial engineering & automation
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
Unsupervised learning
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
BLEU
Subjects
Details
- ISSN :
- 09574174
- Volume :
- 130
- Database :
- OpenAIRE
- Journal :
- Expert Systems with Applications
- Accession number :
- edsair.doi...........1748d1cc977ab4669202b89d6cbedf5d