QBSUM: a Large-Scale Query-Based Document Summarization Dataset from Real-world Applications

• URL: http://arxiv.org/abs/2010.14108v2
• Date: Wed, 28 Oct 2020 08:39:51 GMT
• Title: QBSUM: a Large-Scale Query-Based Document Summarization Dataset from Real-world Applications
• Authors: Mingjun Zhao, Shengli Yan, Bang Liu, Xinwang Zhong, Qian Hao, Haolan Chen, Di Niu, Bowei Long and Weidong Guo
• Abstract summary: We present QBSUM, a high-quality large-scale dataset consisting of 49,000+ data samples for the task of Chinese query-based document summarization. We also propose multiple unsupervised and supervised solutions to the task and demonstrate their high-speed inference and superior performance via both offline experiments and online A/B tests.
• Score: 20.507631900617817
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Query-based document summarization aims to extract or generate a summary of a document which directly answers or is relevant to the search query. It is an important technique that can be beneficial to a variety of applications such as search engines, document-level machine reading comprehension, and chatbots. Currently, datasets designed for query-based summarization are short in numbers and existing datasets are also limited in both scale and quality. Moreover, to the best of our knowledge, there is no publicly available dataset for Chinese query-based document summarization. In this paper, we present QBSUM, a high-quality large-scale dataset consisting of 49,000+ data samples for the task of Chinese query-based document summarization. We also propose multiple unsupervised and supervised solutions to the task and demonstrate their high-speed inference and superior performance via both offline experiments and online A/B tests. The QBSUM dataset is released in order to facilitate future advancement of this research field.

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