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文本生成（NLG）之文本摘要（text summarization）, Extractive text summary of Lead3、keyword、textrank、text teaser、word significance、LDA、LSI。（graph，feature，topic model）

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nlg_yongzhuo		nlg_yongzhuo
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LICENSE		LICENSE
README.md		README.md
requirements.txt		requirements.txt

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nlg-yongzhuo

nlg_yongzhuo

- text_summary
- text_augnment(todo)
- text_generation(todo)
- text_translation(todo)

run(运行, 以text_teaser为例)

- 1. 直接进入目录文件运行即可, 例如进入:nlg_yongzhuo/text_summary/feature_base/
- 2. 运行: python text_teaser.py

nlg_yongzhuo/data

- 数据下载
  ** github项目中只是上传部分数据，需要的前往链接: https://pan.baidu.com/s/1I3vydhmFEQ9nuPG2fDou8Q 提取码: rket

模型与论文paper与地址

pagerank: The PageRank citation ranking: Bringing order to the Web. 1999
textrank: TextRank: Bringing Order into Texts
textteaser: [Automatic Text Summarization for Indonesian Language Using TextTeaser]
significance: The Automatic Creation of Literature Abstracts*
LSI: Text summarization using Latent Semantic Analysis
LDA: Latent Dirichlet Allocation

参考/感谢

文本摘要综述: https://github.com/icoxfog417/awesome-text-summarization
textteaser: https://github.com/IndigoResearch/textteaser
NaiveSumm: https://github.com/amsqr/NaiveSumm
ML主题模型: https://github.com/ljpzzz/machinelearning

*希望对你有所帮助!

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文本生成（NLG）之文本摘要（text summarization）, Extractive text summary of Lead3、keyword、textrank、text teaser、word significance、LDA、LSI。（graph，feature，topic model）

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