Automatic summary of texts in Polish

Abstract

The automatic text summarizing task is one of the most complex problems in the field of natural language processing. In this dissertation, we present the abstraction-based summarization approach which allows to paraphrase the original text and generate new sentences. Creation of new formulations, completely different from the original text is similar to how humans summarize texts. To achieve this, we propose the deep learning method using Sequence to Sequence architecture with the attention mechanism. The goal is to create the model for Polish language, using dataset containing over 200,000 articles from Polish websites, split into text and summary parts. Presented outcomes look promising, obtaining decent results utilizing standard metrics for such type of task.Based on review of prior research done during experiments, this is the very first attempt of applying abstractive text summarization techniques for Polish language.