Adaptation of texts to contextŽontar, Luka (Avtor) Bosnić, Zoran (Mentor) text adaptationcontext-awareartificial intelligencetext summarizationnatural language processingIn this thesis we try to develop a methodology that can adapt texts to target publication types using summarization, natural language generation and paraphrasing. The solution is based on key text characteristics that describe different publication types. To examine types such as social media posts, newspaper articles, research articles and official statements, we use three distinct text evaluation metrics: length, text polarity and readability. While altering key text evaluation metrics, we mostly focus on length due to much research that was done in this field (either with summarization or natural language generation). Using paraphrasing we will try to adjust text readability and polarity that describes reader's negative or positive orientation towards the topic. The process of text adaptation will be implemented iteratively. The developed methodology will automatize writing articles that are based on existing articles. The more crucial contribution of this thesis is that we help to gain access of harder works to those that cannot understand the origin texts.20202020-09-08 14:30:00Diplomsko delo/naloga119412sl