Summarization is the task of compressing source document(s) into coherent and succinct passages. Query-based (contextual) multi-document summarization (qMDS) is a variant that targets summaries to specific informational needs with queries providing additional contexts. Progress in qMDS has been hampered by limited availability of corresponding types of datasets. In this work, we make two contributions. First, we develop an automatic approach for creating both extractive and abstractive qMDS examples from existing language resources. We use this approach to create \qmds, a qMDS dataset for public use. Secondly, to validate the utility of \qmds, we propose a neural model for extractive summarization that exploits the hierarchical nature of the input from multiple documents. It also infuses queries into the modeling to extract query-specific summaries. The experimental results show that modeling the queries and the multiple documents hierarchically improve the performance of qMDS on this datasets. This is consitent with our intuition and supports using \qmds for developing learning methods for qMDS.