  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.
