TextSummariser.py

# importing libraries
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize, sent_tokenize
   
# Input text - to summarize 
text = "There are many techniques available to generate extractive summarization to keep it simple, I will be using an unsupervised learning approach to find the sentences similarity and rank them. Summarization can be defined as a task of producing a concise and fluent summary while preserving key information and overall meaning. One benefit of this will be, you don’t need to train and build a model prior start using it for your project. It’s good to understand Cosine similarity to make the best use of the code you are going to see. Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. Its measures cosine of the angle between vectors. The angle will be 0 if sentences are similar."
   
# Tokenizing the text
stopWords = set(stopwords.words("english")) 
print(stopWords)
words = word_tokenize(text)
print(words)
   
# Creating a frequency table to keep the 
# score of each word
   
freqTable = dict()
for word in words: 
    word = word.lower() 
    if word in stopWords:
        continue
    if word in freqTable:
        freqTable[word] += 1
    else:
        freqTable[word] = 1
   
# Creating a dictionary to keep the score
# of each sentence
sentences = sent_tokenize(text)
print(sentences)
sentenceValue = dict()




print(freqTable)
for sentence in sentences:
    for word, freq in freqTable.items():
        if word in sentence.lower():  
            if sentence in sentenceValue: 
                sentenceValue[sentence] += freq
            else:
                sentenceValue[sentence] = freq
                
#sentenceValue={'First sentence':2+1} 


sumValues = 0
for sentence in sentenceValue:
    sumValues += sentenceValue[sentence]
   
# Average value of a sentence from the original text   
average = int(sumValues / len(sentenceValue))
   
# Storing sentences into our summary.
summary = ''
for sentence in sentences:
    if (sentence in sentenceValue) and (sentenceValue[sentence] > (1.2 * average)):
        summary += " " + sentence
print(summary)