Dealing with vast amounts of textual data requires the use of efficient systems. Automatic summarization systems are capable of addressing this issue. Therefore, it becomes highly essential to work on the design of existing automatic summarization systems and innovate them to make them capable of meeting the demands of continuously increasing data, based on user needs. This study tends to survey the scientific literature to obtain information and knowledge about the recent research in automatic text summarization specifically abstractive summarization based on neural networks. A review of various neural networks based abstractive summarization models have been presented. The proposed conceptual framework includes five key elements identified as encoder-decoder architecture, mechanisms, training strategies and optimization algorithms, dataset, and evaluation metric. A description of these elements is also included in this article. The purpose of this research is to provide an overall understanding and familiarity with the elements of recent neural networks based abstractive text summarization models with an up-to-date review as well as to render an awareness of the challenges and issues with these systems. Analysis has been performed qualitatively with the help of a concept matrix indicating common trends in the design of recent neural abstractive summarization systems. Models employing a transformer-based encoder-decoder architecture are found to be the new state-of-the-art. Based on the knowledge acquired from the survey, this article suggests the use of pre-trained language models in complement with neural network architecture for abstractive summarization task.

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With more accessibility to the Internet and modernization of e-payment systems, the approach to address the travel requirements has dramatically changed over the years. The service offered by the Online Travel Agents (OTAs) has a huge impact on a very competitive online marketplace. The purpose of this research is to observe, examine, and analyze key factors which a consumers’ decision to use a particular travel agent website for e-hotel booking can be predicted. The data are compiled using IBM SPSS. The key methods are applied for data analysis in addition to other statistical methods including factor analysis and multinomial logistic regression analysis. The analysis of data reveals a positive association of website quality factors, product related factors, and consumer relationship factors with consumers’ decision to book from a certain travel agent website. Viewing the big picture, consumer relationship factors are found to be more influential as compared to website quality and product related factors. Moreover, the researchers reveal product price as the most influential variable, but it indicates no statistical significance with consumers’ decision. There are many different price products which are available across different travel agent websites. The convenience of payment method is found to be a significant attribute associated with the consumers’ decision to book from a travel website. In addition, attributes of travel products variety and online reviews provided by the travel websites are observed to be statistically significant. This research indicates the trends of consumer decision-making for e-hotel booking in Indonesia.