QBSUM: A large-scale query-based document summarization dataset from real-world applications

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. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

Query-based document summarization is important for extracting relevant information from documents based on search queries. The lack of publicly available Chinese datasets in this field led to the creation of QBSUM, a high-quality large-scale dataset with proposed solutions to improve performance. This dataset aims to advance research in this area for future developments and applications.