Query-biased Summarization (QBS) aims to produce a query-dependent summary of a retrieved document to reduce the human effort for inspecting the full-text content. Typical summarization approaches extract document snippets that overlap with the query and show them to searchers. Such QBS methods show relevant information in a document but do not inform searchers what is missing. Our study focuses on reducing user effort in finding relevant documents by exposing the information in the query that is missing in the retrieved results. We use a classical approach, DSPApprox, to find terms or phrases relevant to a query. Then, we identify which terms or phrases are missing in a document, present them in a search interface, and ask crowd workers to judge document relevance based on snippets and missing information. Experimental results show both benefits and limitations of our method compared with traditional ones that only show relevant snippets.