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.
