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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.

In general, cloud environments are based on the pay-per-use model, whereby clients pay for the information resources provided by cloud service providers. Users rent and use resources as needed while avoiding the high costs of large-scale resource acquisitions, and providers maximize their profits by managing information resources at a minimum cost while upholding service-level agreements. As the number of resources gradually increases, power supply shortages may arise. This study focuses on the fact that the CPU utilization rate of the server running in the data center is less than 30% and idle servers running only the OS consume more than half of the power consumed by hosts running with maximum CPU utilization and speed. Therefore, this study proposes an approach to enhance data center efficiency through improved management of energy consumption. We present a method to minimize energy consumption while processing the same workload, i.e., ultimately reducing the energy consumed by operating servers. Energy consumption and SLA violation rate were used as evaluation metrics of the optimization model to meet the minimum performance target.