Abstract: A device, system and method for providing descriptions to communication devices using machine learning generated templates is provided. A device replaces given word types in provided text files with corresponding tags to generate corresponding intermediate templates, the provided text files associated with a given topic. The device generates, for the given topic, one or more textual templates that include at least a portion of the corresponding tags, the textual templates in natural language sentences, the generating of the textual templates at least partially based on the corresponding intermediate templates. The device populate the corresponding tags in a textual template, of the textual templates, with corresponding words of a given data file associated with the given topic, to generate a respective description of a given item associated with the given topic, the given data file being specific to the given item.

Abstract: A device, system and method for providing descriptions to communication devices using machine learning generated templates is provided. A device replaces given word types in provided text files with corresponding tags to generate corresponding intermediate templates, the provided text files associated with a given topic. The device generates, for the given topic, one or more textual templates that include at least a portion of the corresponding tags, the textual templates in natural language sentences, the generating of the textual templates at least partially based on the corresponding intermediate templates. The device populate the corresponding tags in a textual template, of the textual templates, with corresponding words of a given data file associated with the given topic, to generate a respective description of a given item associated with the given topic, the given data file being specific to the given item.

Abstract: An abstractive technique and an extractive technique are used to generate concise natural-language summaries of related text documents. The abstractive step generates a machine summary by constructing a graph with nodes representing unique pairs of tokens and corresponding parts-of-speech (POS), and with edge sequences representing token/POS pairs comprising sentences of a corresponding topic group from the text documents. Ranked candidate summary sentences are generated using subgraphs of the graph having initial and final nodes corresponding with valid sentence start and end pairs. The machine summary includes representative summary sentence(s) selected from each topic group's ranked candidates. The extractive step generates a natural-language summary from the machine summary by computing, for each topic group, numerical suitability measures providing comparisons between the representative summary sentence and sentences of the topic group.