Text Summarizer Using Extractive Method
Text Summarizer Using Extractive Method

Simple Text Summarizer Using Extractive Method

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Make simple and short summary of the given article with the help of Text Summarizer using Extractive Method
Text Summarizer Using Extractive Method
Summary of article is created using NLP

Have you seen applications like inshorts that converts the articles or news into 60 words summary. Yes, that’s what we are going to build today. In this article, we will build a text summarizer using extractive method. It is super easy to build and very reliable when it comes to results. Don’t worry, I will also explain what this extracted method is?

You may found many articles about text summarizer using Extractive method but what makes this article unique is the short and beginners friendly high-level description of the code snippets.

So, Text Summarization can be done in two ways:-

  • Extractive method — Selecting the n numbers of most important sentences from the article that most probably convey the message of the article. This approach is very easy to implement and beginners friendly. That’s the main reason of choosing this method for this tutorial.
  • Abstractive method — This method uses concepts of deep learning like encoder-decoder architecture, LSTM(Long Short Term Memory) networks . Those are very difficult for beginners to understand. This method generates the whole new summary of the article and contains sentences that are not even present in the original article. This method may lead to the creation of sentences that don’t have any meaning at all.

Now, as we are clear about why we have chosen the extracted method, Lets directly jump to the coding section 😎

Prerequisites:-

I assume that you are familiar with python and already have installed the python 3 in your systems. I have used jupyter notebook for this tutorial. You can use the IDE of your like.

Installing Required Libraries

For this project, you need to have the following packages installed in your python. If they are not installed, you can simply use pip install PackageName . The scraping part is optional, you can also skip that and use any local text file for which you want a summary.

  • bs4 — for BeautifulSoup that is used for parsing Html page.
  • lxml — it is the package that is used to process Html and XML with python.
  • nltk — for performing natural language processing tasks.
  • urllib — for requesting a webpage.

Let’s Start Coding!

Here, firstly we have import all the libraries that we will use. bs4 and urllib will be used for scraping of the article. re(regular expression) is used for removing unwanted text from the article. The 4th line is used to install the nltk(natural language toolkitpackage. It is the most important package for this tutorial. After that, we have downloaded some of the data that is required for the text processing like punkt (used for sentence tokenizing) and stopwords(words like is,the,of that does not contribute).

import bs4
import urllib.request as url
import re
#!pip3 install nltk
import nltk
nltk.download('punkt')
nltk.download('stopwords')
from nltk import sent_tokenize
from nltk.corpus import stopwords
from nltk import word_tokenize
stop_word = stopwords.words('english')
import string

Here, I have simply taken the URL of the article from the user itself.

url_name = input("Enter url of the text you want to summerize:")

In this snippet of code, we have requested the page source with urllib and then parse that page with BeautifulSoup to find the paragraph tags and added the text to the article variable.

web = url.urlopen(url_name)
page = bs4.BeautifulSoup(web,'html.parser')
elements = page.find_all('p')
article = ''
for i in elements:
article+= (i.text)
article

Now, we remove all the special characters from that string variable article that contains the whole article that is to be summarized. For this, we have simply used inbuilt replace function and also used a regular expression (re) to remove numbers.

processed = article.replace(r'^\s+|\s+?$','')
processed = processed.replace('\n',' ')
processed = processed.replace("\\",'')
processed = processed.replace(",",'')
processed = processed.replace('"','')
processed = re.sub(r'\[[0-9]*\]','',processed)
processed

Here, we have simply used the sent_tokenize function of nltk to make the list that contains sentences of the article at each index.

sentences = sent_tokenize(processed)

After that, we convert the characters of the article to lowercase. Then we loop through every word of the article and check if it is not a stopword or any punctuation. We have already removed the punctuations but we still use this just in case. And if the word is none of them we just added that word into the dictionary and then further count the frequency of that word.

In the screenshot, you can see the dictionary containing every word with its count in the article. The higher the frequency of the word, more important it is. Now you know why we have removed stopwords like of the for otherwise, they will come on top.

frequency = {}
processed1 = processed.lower()
for word in word_tokenize(processed1):
if word not in stop_word and word not in string.punctuation:
if word not in frequency.keys():
frequency[word]=1
else:
frequency[word]+=1
frequency
A dictionary containing every word of article with its frequency
A dictionary containing every word of article with its frequency

Here, we have calculated the importance of every word in the dictionary by simply dividing the frequency of every word with the maximum frequency among them. In the screenshot, you can clearly see that importance of word language comes on top as it has the max frequency that is 22.

max_fre = max(frequency.values())
for word in frequency.keys():
frequency[word]=(frequency[word]/max_fre)
frequency
Importance of every word of the article
Importance of every word of the article

After doing that, now we have to calculate the importance of every sentence of the article. And for doing this, we iterate through every sentence of the article, then for every word in the sentence added the individual score or importance of the word to give the final score of that particular sentence.

In the screenshot, you can clearly see that every sentence now has some score that represents how important that sentence is.

sentence_score = {}
for sent in sentences:
for word in word_tokenize(sent):
if word in frequency.keys():
if len(sent.split(' '))<30:
if sent not in sentence_score.keys():
sentence_score[sent] = frequency[word]
else:
sentence_score[sent]+=frequency[word]
sentence_score
Score or importance of every sentence
Score or importance of every sentence

In the end, We have used heapq to find the 4 sentences with the highest scores. You can choose any number of sentences you want. Then simply joined the list of selected sentences to form a single string of summary.

The final output summary for the Natural Language Processing article can be seen in the screenshot attached.

import heapq
summary = heapq.nlargest(4,sentence_score,key = sentence_score.get)
summary = ' '.join(summary)
final = "SUMMARY:- \n " +summarytextfinal = 'TEXT:- '+processed
textfinal = textfinal.encode('ascii','ignore')
textfinal = str(textfinal)
final
extracted summary with Text Summarizer using Extractive MEthod
Final extracted summary
YES! WE DID IT.

YES!! We have build our Text Summarizer using Extractive Method. The extracted summary may be not up to the mark but it is capable enough of conveying the main idea of the given article. Also, it is more reliable as it only outputs the selected number of sentences from the article itself rather than generating the output of its own.

I will also try to make the tutorial for the abstractive method, but that will be a great challenge for me to explain. 😬

Thank you for your precious time.😊And I hope you like this tutorial.

Also, checkout my tutorial on Movie Review System Using Sentimental Analysis

Pushkara

Woring in the field of I.T. for past 3 years and have expertise in the areas of Machine Learning, App Development(Flutter) and Automation. He loves to write about new technologies and simple projects that can help beginners to start with.

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