add TextSummarization

Author: dfounderliu

.idea/encodings.xml

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+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Serverless directories
+.serverless

TextSummarization/.gitignore

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+# 自动文本摘要Demo
+
+## Demo目的
+
+通过该例子，希望用户可以创建项目的同时，将所需依赖打包上传。
+
+## 提前准备
+
+* 需要电脑安装Node.js，NPM等必要基础软件
+
+* 需要电脑安装Serverless Framework，[可以点击此处获取安装方法](https://github.com/serverless-tencent/serverless-tencent-scf/blob/master/docs/zh/%E5%BF%AB%E9%80%9F%E5%AE%89%E8%A3%85.md)
+
+* 配置账号信息，[可以点击此处获取配置方法](https://github.com/serverless-tencent/serverless-tencent-scf/blob/master/docs/zh/%E9%85%8D%E7%BD%AE%E8%B4%A6%E5%8F%B7.md)
+
+## 使用方法
+
+* 下载本demo到本地
+
+* 进入到Demo目录
+
+* 执行指令`npm install`安装必要的依赖
+
+* 执行命令`pip install -t . jieba snownlp`安装函数运行时所需的依赖
+
+* 执行`serverless deploy`进行部署，部署成功可以看到以下信息：
+
+```text
+Serverless: Packaging service...
+Serverless: Excluding development dependencies...
+Serverless: Uploading service package to cos[sls-cloudfunction-ap-guangzhou]. textsummarization-dev-YbdGk3-2019-11-05-23-50-43.zip
+Serverless: Uploaded package successful /Users/dfounderliu/Desktop/SeerverlessPluginDemo/TextSummarization/.serverless/textsummarization.zip
+Serverless: Creating function textsummarization-dev-text_summarization
+Serverless: Created function textsummarization-dev-text_summarization
+Serverless: Setting tags for function textsummarization-dev-text_summarization
+Serverless: Creating trigger for function textsummarization-dev-text_summarization
+Serverless: Deployed function textsummarization-dev-text_summarization successful
+Serverless: Service Information
+service: textsummarization 
+stage: dev 
+region: ap-guangzhou 
+stack: textsummarization-dev
+resources: 1
+functions:   text_summarization: textsummarization-dev-text_summarization
+
+```
+
+* 完成之后，可以通过invoke实现远程调试`serverless invoke -f text_summarization --path test.json`:
+```text
+Serverless: 
+
+{"keywords": ["\u51fd\u6570", "\u817e\u8baf", "\u8fd0\u884c", "\u81ea\u5b9a\u4e49", "COS"], "summary": ["\u60a8\u53ea\u9700\u7f16\u5199\u7b80\u5355\u7684\u3001\u76ee\u7684\u5355\u4e00\u7684\u4e91\u51fd\u6570\u5373\u53ef\u5c06\u5b83\u4e0e\u60a8\u7684\u817e\u8baf\u4e91\u57fa\u7840\u8bbe\u65bd\u53ca\u5176\u4ed6\u4e91\u670d\u52a1\u4ea7\u751f\u7684\u4e8b\u4ef6\u5173\u8054", "\u4e91\u51fd\u6570\u5728\u6267\u884c\u65f6\u5c06\u6839\u636e\u8bf7\u6c42\u8d1f\u8f7d\u6269\u7f29\u5bb9", "\u817e\u8baf\u4e91\u4e91\u51fd\u6570\u662f\u817e\u8baf\u4e91\u63d0\u4f9b\u7684 Serverless \u6267\u884c\u73af\u5883", "\u5728 COS Bucket \u4e0a\u4f20\u65f6\u3001\u5220\u9664\u6587\u4ef6\u65f6\u8fd0\u884c\u4e91\u51fd\u6570\u3001\u5e94\u7528\u7a0b\u5e8f\u901a\u8fc7 SDK \u8c03\u7528\u65f6\u8fd0\u884c\u4e91\u51fd\u6570", "\u53ea\u9700\u4e3a\u8fd0\u884c\u4e2d\u7684\u4e91\u51fd\u6570\u4ed8\u8d39"]}
+
+----------
+Log: 
+START RequestId: 69ff3f4a-ffe4-11e9-a60a-525400cdceed
+Event RequestId: 69ff3f4a-ffe4-11e9-a60a-525400cdceed
+Building prefix dict from the default dictionary ...
+Building prefix dict from the default dictionary ...
+Dumping model to file cache /tmp/jieba.cache
+Dumping model to file cache /tmp/jieba.cache
+Loading model cost 1.092 seconds.
+Loading model cost 1.092 seconds.
+Prefix dict has been built succesfully.
+Prefix dict has been built succesfully.
+
+END RequestId: 69ff3f4a-ffe4-11e9-a60a-525400cdceed
+Report RequestId: 69ff3f4a-ffe4-11e9-a60a-525400cdceed Duration:1237.17ms Memory:1024MB MaxMemoryUsed:658.383MB
+
+```

TextSummarization/README.md

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+# -*- coding: utf8 -*-
+import logging
+import jieba.analyse
+from snownlp import SnowNLP
+
+logging.basicConfig(level=logging.NOTSET)
+
+
+def FromSnowNlp(text, summary_num):
+    s = SnowNLP(text)
+    return s.summary(summary_num)
+
+
+def FromJieba(text, keywords_type, keywords_num):
+    if keywords_type == "tfidf":
+        return jieba.analyse.extract_tags(text, topK=keywords_num)
+    elif keywords_type == "textrank":
+        return jieba.analyse.textrank(text, topK=keywords_num)
+    else:
+        return None
+
+
+def main_handler(event, context):
+    text = event["text"]
+    summary_num = event["summary_num"]
+    keywords_num = event["keywords_num"]
+    keywords_type = event["keywords_type"]
+    return {"keywords": FromJieba(text, keywords_type, keywords_num),
+            "summary": FromSnowNlp(text, summary_num)}