Related papers: QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization

QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization

URL: http://arxiv.org/abs/2104.05938v1
Date: Tue, 13 Apr 2021 05:00:35 GMT
Title: QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization
Authors: Ming Zhong, Da Yin, Tao Yu, Ahmad Zaidi, Mutethia Mutuma, Rahul Jha, Ahmed Hassan Awadallah, Asli Celikyilmaz, Yang Liu, Xipeng Qiu, Dragomir Radev
Abstract summary: QMSum consists of 1,808 query-summary pairs over 232 meetings in multiple domains. We investigate a locate-then-summarize method and evaluate a set of strong summarization baselines on the task.
Score: 45.83402681068943
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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}.

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