Before revision: The availability of large-scale datasets has driven the development of neural sequence-to-sequence models to generate generic summaries , i. e., summaries which do not correspond to any pre-specified queries.
After revision: The availability of large-scale datasets has driven the development of neural models that create generic summaries from single or multiple documents. In this work we consider query focused summarization (QFS), a task for which training data in the form of queries, documents, and summaries is not readily available. We propose to decompose QFS into (1) query modeling (i. e., summaries which do not correspond to any pre-specified queries.