  Meetings are a key component of human collaboration. As increasing numbers of
meetings are recorded and transcribed, meeting summaries have become essential
to remind those who may or may not have attended the meetings about the key
decisions made and the tasks to be completed. However, it is hard to create a
single short summary that covers all the content of a long meeting involving
multiple people and topics. In order to satisfy the needs of different types of
users, we define a new query-based multi-domain meeting summarization task,
where models have to select and summarize relevant spans of meetings in
response to a query, and we introduce QMSum, a new benchmark for this task.
QMSum consists of 1,808 query-summary pairs over 232 meetings in multiple
domains. Besides, we investigate a locate-then-summarize method and evaluate a
set of strong summarization baselines on the task. Experimental results and
manual analysis reveal that QMSum presents significant challenges in long
meeting summarization for future research. Dataset is available at
\url{https://github.com/Yale-LILY/QMSum}.
