LCSTS: A Large Scale Chinese Short Text Summarization Dataset
This work addresses the problem of data scarcity for researchers in automatic text summarization, particularly for Chinese short texts, though it is incremental as it focuses on dataset creation and a baseline method.
The authors tackled the lack of large-scale datasets for text summarization by introducing LCSTS, a dataset of over 2 million Chinese short texts from Sina Weibo with author-written summaries, and manually tagged relevance for 10,666 summaries. They used a recurrent neural network for summary generation, achieving promising results that demonstrate the dataset's usefulness and provide a baseline for future research.
Automatic text summarization is widely regarded as the highly difficult problem, partially because of the lack of large text summarization data set. Due to the great challenge of constructing the large scale summaries for full text, in this paper, we introduce a large corpus of Chinese short text summarization dataset constructed from the Chinese microblogging website Sina Weibo, which is released to the public {http://icrc.hitsz.edu.cn/Article/show/139.html}. This corpus consists of over 2 million real Chinese short texts with short summaries given by the author of each text. We also manually tagged the relevance of 10,666 short summaries with their corresponding short texts. Based on the corpus, we introduce recurrent neural network for the summary generation and achieve promising results, which not only shows the usefulness of the proposed corpus for short text summarization research, but also provides a baseline for further research on this topic.