Text Summarizer
大小:0.25MiB版本:v 0.0.0.1更新时间:2017-01-07
This extension takes inputted text and summarizes the text using the TextRank algorithm
Text Summarizer 的使用方法详解,最全面的教程
Text Summarizer 描述:
用户数:2000
分类:生产工具插件
扩展大小:0.25M
最后更新时间:2017-01-07
版本:v 0.0.0.1
Text Summarizer 插件简介:
这是来自Chrome商店的 Text Summarizer 浏览器插件,您可以在当前页面下载它的最新版本安装文件,并安装在Chrome、Edge等浏览器上。
Text Summarizer插件下载方法/流程:
点击下载按钮,关注“扩展迷Extfans”公众号并获取验证码,在网页弹窗中输入验证码,即可下载最新安装文件。
Text Summarizer插件安装教程/方法:
(1)将扩展迷上下载的安装包文件(.zip)解压为文件夹,其中类型为“crx”的文件就是接下来需要用到的安装文件
(2) 从设置->更多工具->扩展程序 打开扩展程序页面,或者地址栏输入 Chrome://extensions/ 按下回车打开扩展程序页面
(3) 打开扩展程序页面的“开发者模式”
(4) 将crx文件拖拽到扩展程序页面,
完成安装如有其它安装问题,
请扫描网站底部二维码与客服联系如有疑问请参考:
https://www.extfans.com/installation/This extension takes text and summarizes the text using the TextRank algorithm. The TextRank algorithm is actually based on Google's early PageRank algorithm, which revolutionized how we viewed webpages by assigning importance to links in a set. We use the same basic technology to rank sentences as being more or less important and return the more important sentences.The user has 4 parameters he or she can set if he or she chooses. The number of iterations is the number of times the TextRank algorithm will be run. The idea is that more iterations will produce a better answer, at the cost of efficiency and time. 40 iterations is the default value. There are two other factors which are a bit more confusing: damping factor and delta. The damping rate makes the changing of weights in between iterations less dramatic and delta defines a threshold under which the response is satisfactory. These parameters do not need to be changed by the user but the user can change them if he or she wants. In a paper by Mihalcea and Tarau, describing the algorithm, they state that the value for the damping factor is set to 0.85, which is what we have set it to. The pre-processing on the sentences inputted into the algorithm is limited but sufficient (trim function). The inputted text is pre-processed and made into a graph of frequencies for each word in each sentence over all the words in the document. We then run the PageRank algorithm on the set and get back the sentences and their scores. We sort according to scores and return the number of sentences by scores requested by the user, essentially summarizing the text by importance and relevance.