LCSTS: A Large Scale Chinese Short Text Summarization Dataset

TL;DR

This paper introduces a large-scale Chinese short text summarization dataset from Sina Weibo, enabling research in automatic summarization with over 2 million texts and providing baseline results using recurrent neural networks.

Contribution

The paper presents a new large-scale Chinese short text summarization dataset and demonstrates its utility with baseline neural network models.

Findings

The dataset contains over 2 million Chinese texts with summaries.

Recurrent neural networks achieve promising results on the dataset.

The dataset facilitates future research in Chinese short text summarization.

Abstract

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.