We present BAYE S UM (for “Bayesian summarization”), a model for sentence extraction in query-focused summarization. BAYE S UM leverages the common case in which multiple documents are relevant to a single query. Using these documents as reinforcement for query terms, BAYE S UM is not afflicted by the paucity of information in short queries. We show that approximate inference in BAYE S UM is possible on large data sets and results in a stateof-the-art summarization system. Furthermore, we show how BAYE S UM can be understood as a justified query expansion technique in the language modeling for IR.
