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}.
@article{arxiv.2104.05938,
title = {QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization},
author = {Ming Zhong and Da Yin and Tao Yu and Ahmad Zaidi and Mutethia Mutuma and Rahul Jha and Ahmed Hassan Awadallah and Asli Celikyilmaz and Yang Liu and Xipeng Qiu and Dragomir Radev},
journal= {arXiv preprint arXiv:2104.05938},
year = {2021}
}