Abstract: A method, computer system, and a computer program product for providing an automated graphical and textual review summary of user submissions has been provided. In one embodiment this comprises obtaining a plurality of user submissions relating to a particular subject matter and having information relating to this subject matter. Extracting at least one feature and feature review relating to each of the user submissions and generating statistical insights relating to these features and features reviews and other relevant information relating. A summary review is then generated relating to these user submissions. The summary review includes information about each feature, feature review and statistical insights.

Abstract: A method, computer system, and a computer program product for persona-based navigation is provided. The present invention may include creating a persona. The present invention may include developing a routing system. The present invention may include enabling a user with at least one route option based on the created persona. The present invention may include displaying a selected route.

Abstract: A computer implemented method, computer program product, and computer system technique for optimizing automated teller machine (ATM) replenishment schedules to prevent an ATM from having insufficient product and also to prevent the inefficient use of product. The technique involves retrieving unstructured data from the users of the ATM. Then the technique analyzes the unstructured data using a natural language processing (NLP) model. The technique then inputs the results of the natural language processing deep neural network (DNN) trained with structured data. The result of the NLP and DNN analysis is a user persona. The user persona is used to generate the optimized ATM replenishment schedule.

Abstract: A computer implemented method, computer program product, and computer system technique for optimizing automated teller machine (ATM) replenishment schedules to prevent an ATM from having insufficient product and also to prevent the inefficient use of product. The technique involves retrieving unstructured data from the users of the ATM. Then the technique analyzes the unstructured data using a natural language processing (NLP) model. The technique then inputs the results of the natural language processing deep neural network (DNN) trained with structured data. The result of the NLP and DNN analysis is a user persona. The user persona is used to generate the optimized ATM replenishment schedule.

Abstract: An enhanced text summarizer is provided. A processor receives a request to generate a summarization of a text. A processor determines at least one domain associated with the text. A processor extracts one or more features from the text. A processor determines one or more relevance scores for the one or more features based on the determined at least one domain associated with the text. A processor determines a feature-based ranking for one or more sentences of the text. A processor determines an extractive-based ranking for one or more sentences of the text. A processor merges at least one sentence of the feature-based ranking and at least one sentence of the extractive-based ranking of the text. A processor generates the summarization of the text based on the merged the at least one sentence of the feature-based ranking and the at least one sentence of the extractive-based ranking of the text.

Abstract: An enhanced text summarizer is provided. A processor receives a request to generate a summarization of a text. A processor determines at least one domain associated with the text. A processor extracts one or more features from the text. A processor determines one or more relevance scores for the one or more features based on the determined at least one domain associated with the text. A processor determines a feature-based ranking for one or more sentences of the text. A processor determines an extractive-based ranking for one or more sentences of the text. A processor merges at least one sentence of the feature-based ranking and at least one sentence of the extractive-based ranking of the text. A processor generates the summarization of the text based on the merged the at least one sentence of the feature-based ranking and the at least one sentence of the extractive-based ranking of the text.