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bit-nlp-p2's Introduction

文本自动摘要

BIT自然语言理解初步大作业2 项目部署:www.0x404.tech

项目使用说明

直接在线访问本应用

  • 本项目为web项目,项目已部署到云服务器,可通过网址http://www.0x404.tech 直接在线访问与使用本应用。
  • 建议使用google游览器或者edge游览器
  • 本文档为本地部署教程。

项目本地运行

下载项目

 #克隆github仓库到本地
 git clone [email protected]:0x404/BIT-NLP-P2.git
 
 #进入Django项目目录下
 cd BIT-NLP-P2/django_online/

需要的资源下载

由于使用的词向量表示和训练数据集过大,并没有作为提交文件的一部分提交,故如需在本地进行部署,请完成如下资源的下载:

  • 基于微博语料库训练的$300$维词向量
  • NLPCC2017摘要数据

使用命令行命令自动下载(推荐)

使用脚本自动下载所需的数据文件,执行下列命令后无需手动下载资源,可以直接运行本项目。

#如果python没有wget库则先下载该库
pip install wget

#使用脚本下载语料库
python make.py

手动下载

下载完成将如上资源放在目录django_online/keyword_extraction/textrank/data中即可

运行本Web应用

#运行程序
python manage.py runserver

#Starting development server at http://127.0.0.1:8000/

在浏览器地址栏输入http://127.0.0.1:8000/本地访问项目。

任务

  • textRank计算关键词
  • textRank计算关键句
  • web前端HTML界面
  • Django后端
  • transformer

待做

  • 检查一个词在停用词表中 用字典树优化
  • 计算关键句中 用字典树优化
  • 更细粒度的分句
  • textrank可以进一步改进,如加入句子长度的惩罚,或者使用句向量判断相似性

参考资料来源

bit-nlp-p2's People

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