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Streaming data poses a variety of new and interesting challenges for information retrieval and text analysis. Unlike static document collections, which are typically analyzed and indexed off-line to support ad-hoc queries, streaming data often must be analyzed on the fly and acted on as the data passes through the analysis system. Speech is one example of streaming data that is a challenge to exploit, yet has significant potential to provide value in a knowledge management system. We are specifically interested in techniques that analyze streaming data and automatically find collateral information, or information that clarifies, expands, and generally enhances the value of the streaming data. We present a system that analyzes a data stream and automatically finds documents related to the current topic of discussion in the data stream. Experimental results show that the system generates result lists with an average precision at 10 hits of better than 60%. We also present a hit-list re-ranking technique based on named entity analysis and automatic text categorization that can improve the search results by 6%---12%.