This article presents a new conceptual approach for the interpretative topic modeling problem. It uses sentences as basic units of analysis, instead of words or n-grams, which are commonly used in the standard approaches.The proposed approach’s specifics are using sentence probability evaluations within the text corpus and clustering of sentence embeddings. The topic model estimates discrete distributions of sentence occurrences within topics and discrete distributions of topic occurrence within the text. Our approach provides the possibility of explicit interpretation of topics since sentences, unlike words, are more informative and have complete grammatical and semantic constructions inside. The method for automatic topic labeling is also provided. Contextual embeddings based on the BERT model are used to obtain corresponding sentence embeddings for their subsequent analysis. Moreover, our approach allows big data processing and shows the possibility of utilizing the combination of internal and external knowledge sources in the process of topic modeling. The internal knowledge source is represented by the text corpus itself and often it is a single knowledge source in the traditional topic modeling approaches. The external knowledge source is represented by the BERT, a machine learning model which was preliminarily trained on a huge amount of textual data and is used for generating the context-dependent sentence embeddings.