Text summarization allows us to extract useful information from a vast amount of textual documents. For example, during research we want to simplify the paper selection process by reading only abstracts instead of whole articles. In this thesis we focus on the problem of summarization of Slovene texts. Our goal is to generate an accurate and readable summary. We tackle the problem by applying a Sequence2Sequence architecture and deep neural networks. We developed nine models, which differ from one another by the type of recurrent cells, number of recurrent cells, number of levels and additional mechanisms, such as attention and copying. For evaluation we used ROUGE and BERTScore evaluation metrics. Our most succesful model produces the best results among Slovene text summarizers.
|
