LCSTS

Introduction

LCSTS is a Large-scale Chinese Short Text Summarization dataset constructed from the Chinese microblogging website SinaWeibo for the summary generation, which is collected by Harbin Institute of Technology. This corpus consists of over 2 million real Chinese short texts with short summaries given by the writer of each text, as well as 10,666 short summaries marked manually.

Paper

LCSTS: A Large Scale Chinese Short Text Summarization Dataset. EMNLP 2015.

Data Size

Training set: 2,400,591; Validation set: 8,685; Test set: 725.

Data Format

Each instance is composed of a human-labeled summary quality score (human_label, an integer), input text (text, a string) and a output summary (summary, an integer).

Example

{
    "human_label": 5, 
    "summary": "林志颖公司疑涉虚假营销无厂房无研发", 
    "text": "日前，方舟子发文直指林志颖旗下爱碧丽推销假保健品，引起哗然。调查发现，爱碧丽没有自己的生产加工厂。其胶原蛋白饮品无核心研发，全部代工生产。号称有“逆生长”功效的爱碧丽“梦幻奇迹限量组”售价>高达1080元，实际成本仅为每瓶4元！"
}

"human_label" (int): the human-labeled summary quality score(Only the validation set and the test set have this label, and the data set only includes 3, 4, and 5 points data, not including 1, 2 points data.).
"text" (str): input text.
"summary"（str): a output summary.

Evaluation Code

The prediction result needs to be consistent with the format of the evaluation code.

Dependency packages: rouge==1.0.0, jieba=0.42.1

python eval.py prediction_file test_private_file

The evaluation metrics are rouge-1, rouge-2, rouge-l, and the output is in dictionary format.

return {
    "rouge-1-f": _, 
    "rouge-1-p": _, 
    "rouge-1-r": _, 
    "rouge-2-f": _, 
    "rouge-2-p": _, 
    "rouge-2-r": _, 
    "rouge-l-f": _, 
    "rouge-l-p": _, 
    "rouge-l-r": _}

Author List

Baotian Hu, Qingcai Chen, Fangze Zhu

Institutions

Harbin Institute of Technology

Citation

@inproceedings{hu2015lcsts,
  title={LCSTS: A Large Scale Chinese Short Text Summarization Dataset},
  author={Hu, Baotian and Chen, Qingcai and Zhu, Fangze},
  booktitle={Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
  pages={1967--1972},
  year={2015}
}