LCSTS: A Large Scale Chinese Short Text Summarization Dataset

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1967–1972, Lisbon, Portugal, 17-21 September 2015. c⃝2015 Association for Computational Linguistics. LCSTS: A Large Scale Chinese Short Text Summarization Dataset Baotian Hu Qingcai Chen Fangze Zhu Intelligent Computing Research Center Harbin Institute of Technology, Shenzhen Graduate School {baotianchina,qingcai.chen, zhufangze123}@gmail.com 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 pa- per, we introduce a large corpus of Chi- nese short text summarization dataset con- structed from the Chinese microblogging website Sina Weibo, which is released to the public1. 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 cor- pus for short text summarization research, but also provides a baseline for further re- search on this topic. 1 Introduction Nowadays, individuals or organizations can eas- ily share or post information to the public on the social network. Take the popular Chinese mi- croblogging website (Sina Weibo) as an example, the People’s Daily, one of the media in China, posts more than tens of weibos (analogous to tweets) each day. Most of these weibos are well- written and highly informative because of the text length limitation (less than140 Chinese charac- ters). Such data is regarded as naturally annotated web resources (Sun, 2011). If we can mine these high-quality data from these naturally annotated web resources, it will be beneficial to the research that has been hampered by the lack of data. 1http://icrc.hitsz.edu.cn/Article/show/139.html Figure 1: A Weibo Posted by People’s Daily. In the Natural Language Processing (NLP) community, automatic text summarization is a hot and difficult task. A good summarization system should understand the whole text and re-organize the information to generate coherent, informative, and significantly short summaries which convey important information of the original text (Hovy and Lin, 1998), (Martins, 2007). Most of tradi- tional abstractive summarization methods divide the process into two phrases (Bing et al., 2015). First, key textual elements are extracted from the original text by using unsupervised methods or lin- guistic knowledge. And then, unclear extracted components are rewritten or paraphrased to pro- duce a concise summary of the original text by using linguistic rules or language generation tech- niques. Although extensive researches have been done, the linguistic quality of abstractive sum- mary is still far from satisfactory. Recently, deep learning methods have shown potential abilities to learn representation (Hu et al., 2014; Zhou et al., 2015) and generate language (Bahdanau et al., 2014; Sutskever et al., 2014) from large scale data by utilizing GPUs. Many researchers realize that we are closer to …

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