  The abstractive methods lack of creative ability is particularly a problem in
automatic text summarization. The summaries generated by models are mostly
extracted from the source articles. One of the main causes for this problem is
the lack of dataset with abstractiveness, especially for Chinese. In order to
solve this problem, we paraphrase the reference summaries in CLTS, the Chinese
Long Text Summarization dataset, correct errors of factual inconsistencies, and
propose the first Chinese Long Text Summarization dataset with a high level of
abstractiveness, CLTS+, which contains more than 180K article-summary pairs and
is available online. Additionally, we introduce an intrinsic metric based on
co-occurrence words to evaluate the dataset we constructed. We analyze the
extraction strategies used in CLTS+ summaries against other datasets to
quantify the abstractiveness and difficulty of our new data and train several
baselines on CLTS+ to verify the utility of it for improving the creative
ability of models.
