This paper introduces a novel approach for document re-ranking in information retrieval based on topic-comment structure of texts. While most information retrieval models make the assumption that relevant documents are about the query and that aboutness can be captured considering bags of words only, we rather consider a more sophisticated analysis of discourse to capture document relevance by distinguishing the topic of a text from what is said about the topic (comment) in the text. The topic-comment structure of texts is extracted automatically from the first retrieved documents which are then re-ranked so that the top documents are the ones that share their topics with the query. The evaluation on TREC collections shows that the method significantly improves the retrieval performance.