# LCSTS: A Large Scale Chinese Short Text Summarization Dataset

arXiv: https://arxiv.org/abs/1506.05865 · HTML: https://mlsanity.com/paper/1506.05865/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2015.

## Built on by (most cited first, 30 of 65)

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- [Evaluating the Efficacy of Summarization Evaluation across Languages](https://mlsanity.com/paper/2106.01478/) (2021)
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- [Summarising Historical Text in Modern Languages](https://mlsanity.com/paper/2101.10759/) (2021)
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## Abstract (arXiv)

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.