  Automatic text summarization aims to produce a brief but crucial summary for
the input documents. Both extractive and abstractive methods have witnessed
great success in English datasets in recent years. However, there has been a
minimal exploration of text summarization in Chinese, limited by the lack of
large-scale datasets. In this paper, we present a large-scale Chinese news
summarization dataset CNewSum, which consists of 304,307 documents and
human-written summaries for the news feed. It has long documents with
high-abstractive summaries, which can encourage document-level understanding
and generation for current summarization models. An additional distinguishing
feature of CNewSum is that its test set contains adequacy and deducibility
annotations for the summaries. The adequacy level measures the degree of
summary information covered by the document, and the deducibility indicates the
reasoning ability the model needs to generate the summary. These annotations
can help researchers analyze and target their model performance bottleneck. We
examine recent methods on CNewSum and release our dataset to provide a solid
testbed for automatic Chinese summarization research.
