CLTS+: A New Chinese Long Text Summarization Dataset with Abstractive Summaries
This addresses the problem of limited abstractive summarization data for Chinese NLP researchers, though it is incremental as it builds on an existing dataset.
The authors tackled the lack of abstractive summarization datasets for Chinese by creating CLTS+, a new dataset with over 180K article-summary pairs, and introduced a metric to evaluate its abstractiveness, showing its utility for improving model creativity.
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.