Automated document summarization is one of the more prominent natural language processing tasks. A good summarization method will consider two aspects of text: (1) it's informational structure, so that it retains the informationally relevant and leaves out less informationally relevant parts, and (2) text coherence and cohesion. Two main approaches to automated summarization are extractive summarization, which reuses parts of the original text, and abstractive summarization, which generates new text based on the existing text. Recent developments in deep learning have brought improvements to document summarization, especially for abstractive approaches. The topic of the thesis are models for automated document summarization based on deep learning. Do a literature survey on extractive and abstractive summarization, with an emphasis on deep learning methods. Devise and implement a deep learning model for extractive and abstractive summarization for the English language. Model development and evaluation should rely on freely available datasets, such as DUC and CNN/Daily Mail. Carry out an experimental evaluation of the model, a comparison against the existing models, and a statistical analysis of the results. All references must be cited, and all source code, documentation, executables, and datasets must be provided with the thesis.