This work represents the research of television audience of news programs in Czech Republic during the years 2008 to 2013 in order to capture the possible preference for specific viewers areas of economic events, or information. For the needs of econometric time-series analysis is assembled basic model, which is individually applied to a total of five time series of news programs. Econometric estimates suggest that key events increasing viewership of Czech news programs are riots and demonstrations in the territory of Czech Republic, with a positive effect on the ratings of up to 4.1%, or home catastrophic events that increase viewership up by 4.6%. The world macroeconomic events showed the negative effect of up to 2.4% for viewership of television news.

News companies have a need to automate and make the editors process of writing about hot and new events more effective. Current technologies involve robotic programs that fills in values in templates and website listeners that notifies the editors when changes are made so that the editor can read up on the source change at the actual website. Editors can provide news faster and better if directly provided with abstracts of the external sources. This study applies deep learning algorithms to automatically formulate abstracts and tag sources with appropriate tags based on the context. The study is a full stack solution, which manages both the editors need for speed and the training, testing and validation of the algorithms. Decision Tree, Random Forest, Multi Layer Perceptron and phrase document vectors are used to evaluate the categorisation and Recurrent Neural Networks is used to paraphrase unformatted texts. In the evaluation a comparison between different models trained by the algorithms with a variation of parameters are done based on the F-score. The results shows that the F-scores are increasing the more document the training has and decreasing the more categories the algorithm needs to consider. The Multi-Layer Perceptron perform best followed by Random Forest and finally Decision Tree. The document length matters, when larger documents are considered during training the score is increasing considerably. A user survey about the paraphrase algorithms shows the paraphrase result is insufficient to satisfy editors need. It confirms a need for more memory to conduct longer experiments.