When searching the web, it is often possible that there are too many results available for ambiguous queries. Text snippets, extracted from the retrieved pages, are an indicator of the pages' usefulness to the query intention and can be used to focus the scope of search results. In this paper, we propose a novel method for automatically extracting web page snippets that are highly relevant to the query intention and expressive of the pages' entire content. We show that the usage of semantics, as a basis for focused retrieval, produces high quality text snippet suggestions. The snippets delivered by our method are significantly better in terms of retrieval performance compared to those derived using the pages' statistical content. Furthermore, our study suggests that semantically-driven snippet generation can also be used to augment traditional passage retrieval algorithms based on word overlap or statistical weights, since they typically differ in coverage and produce different results. User clicks on the query relevant snippets can be used to refine the query results and promote the most comprehensive among the relevant documents.