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Welcome to the text-summarizer wiki!

Table of contents

• Introduction
• Installation
• Usage
• Library abberivator_flow of backend
• Tree structure of frontend

1. Introduction

The text-summarizer Software is a tool to analyze text documents and simplify them to only the key words. It captures most significant concepts, providing a concise overview, which can be useful in studying and memorizing for the user. The software is applicable to a myriad of documents such as articles or textbooks; and supports typical text files or online articles.

2. Installation

Warning

If you git clone or download the repos, you will have nightly-build product.

If you download 'Release' section, you will have stable product.

Protocol

pip install -r requirements.txt .

Run this command line in 'modules' folder to set up all packages.

python nltk_downloads.py

Run this command line in 'modules' folder to set up all components related to nltk.

3. Usage

Ignite the app

python text-sum-core.py

Run this command line in 'webapp' folder to start the application.

Open your favorite browser and go to this link.

http://localhost:5000/text-summarizer

Use the app

It's quite easy to use.

The web app will return you as text file named 'out.txt'

Plain text (in English)

You can paste your long text in the first input box then click summarize this.

Wikipedia article (in English)

You can paste a link to an English Wikipedia article such as this or that in the second input box then click summarize this.

https://en.wikipedia.org/wiki/Communist_Party_of_Georgia_(Soviet_Union)
https://en.wikipedia.org/wiki/Loss_function

Documents (Purely created and in English)

You can upload a .pdf, .odt, .docx file then click summarize this.

4. Library abberivator_flow

This library has 3 main modules

from abberivator_flow import scriptum_abberivator as sa
from abberivator_flow import wikipedia_abberivator as wa
from abberivator_flow import document_abberivator as da

long_str = "Imagine you have a paragraph here not this short string"
output_str = sa.summarize(long_str)

url = "https://en.wikipedia.org/wiki/Loss_function"
output_str = wa.summarize(url)

file_path = "your_file.odt" # your_file.docx or your_file.pdf
output_str = da.summarize(file_path)

5. Tree structure of frontend

Tree structure of folder 'webapp'

|   text-sum-core.py
|   
+---static
|   |   a.jpg
|   |   b.jpg
|   |   
|   +---css
|   |       style.css
|   |       
|   \---js
|           main.js
|           
\---templates
        main-page.html