ChineseNLP

Chinese Text Summarization

Background

Text summarization takes a long text document and creates a shorter text document that is a fluent and accurate summary of the longer text document.

Example

Input:

 较早进入中国市场的星巴克， 是不少小资钟 情的品牌。相比在美国的平民形象，星巴克在中国就 显得“高端”得多。用料并无差别的一杯中杯美式咖 啡，在美国仅约合人民币12元，国内要卖21元，相当 于贵了75%。第一财经日报 

Output:

媒体称星巴克美式咖啡售价中国比美国 贵75%。

Standard Metrics

ROUGE compares an automatically produced summary with human-produced, reference summaries. ROUGE-1 records unigram overlap, ROUGE-2 bigram overlap, and ROUGE-L the longest common subsequence. ROUGE can be computed over characters or words.

Implementations

• http://www.berouge.com/Pages/default.aspx
• https://github.com/lancopku/superAE/blob/master/data/script/PythonROUGE.py

LCSTS: A Large Scale Chinese Short Text Summarization Dataset.

• Hu et. al. (2015) created a dataset of Weibo summaries posted by media organizations. Short texts are about 100 characters, summaries are about 20 characters.
• Download instruction: “If you want to acquire the corpus. Please fill the application form and send to Qingcai Chen or Baotian Hu application form, Mainland China application form, Other” (http://icrc.hitsz.edu.cn/Article/show/139.html)

Test set	# (text, summary) pairs	# (text, summary) pairs >= 3 score	Genre
Part II (validation)	10,666	8,685	News, politics, economic, military, movies, games, etc.
Part III (test)	1,106	725	News, politics, economic, military, movies, games, etc.

Results

System	ROUGE-1	ROUGE-2	ROUGE-L
Duan et al. (2019)	44.35	30.65	40.58
Wang et. al. (2018)	39.9	21.5	37.9
Lin et. al. (2018)	39.4	26.9	36.5
Ma et. al. (2018)	39.2	26.0	36.2
Wei et. al. (2018)	36.2	24.3	33.8
Seq2Seq (baseline)	32.1	19.9	29.2

Resources

Train set	# (text, summary) pairs	Genre
Part I	2,400,591	News

Other Resources

• List of Chinese text summarization papers
• MAT-INF dataset (ACL 2020), multi-task dataset that includes a 1M document summarization corpus, and shows improved summarization quality after training on other tasks.
• Recent work Abstractive Text Summarization by Incorporating Reader Comments (AAAI 2019), does not use the LCSTS dataset but their own version of SinaWeibo dataset
• Toolkit for abstractive summarization: https://github.com/lancopku/LancoSum

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