A Large-Scale Chinese Long-Text Extractive Summarization Corpus

Recently, large-scale datasets have vastly facilitated the development in nearly domains of Natural Language Processing. However, lacking large scale Chinese corpus is still a critical bottleneck for further research on deep text summarization methods. In this paper, we publish a large-scale Chinese Long-text Extractive Summarization corpus named CLES. The CLES contains about 104K pairs, which is originally collected from Sina Weibo. To verify the quality of the corpus, we also manually tagged the relevance score of 5,000 pairs. Our benchmark models on the proposed corpus include conventional deep learning based extractive models and several pre-trained Bert-based algorithms. Their performances are reported and briefly analyzed to facilitate further research on the corpus. We will release this corpus for further research.