  Query-focused summarization (QFS) aims to extract or generate a summary of an
input document that directly answers or is relevant to a given query. The lack
of large-scale datasets in the form of documents, queries, and summaries has
hindered model development in this area. In contrast, multiple large-scale
high-quality datasets for generic summarization exist. We hypothesize that
there is a hidden query for each summary sentence in a generic summarization
annotation, and we utilize a large-scale pretrained language model to recover
it. In this way, we convert four generic summarization benchmarks into a new
QFS benchmark dataset, LMGQS, which consists of over 1 million
document-query-summary samples. We thoroughly investigate the properties of our
proposed dataset and establish baselines with state-of-the-art summarization
models. By fine-tuning a language model on LMGQS, we achieve state-of-the-art
zero-shot and supervised performance on multiple existing QFS benchmarks,
demonstrating the high quality and diversity of LMGQS.
