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from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize, sent_tokenize
from nltk.stem.snowball import SnowballStemmer
import nltk
# If you get an error uncomment this line and download the necessary libraries
text = """ """
stemmer = SnowballStemmer("english")
stopWords = set(stopwords.words("english"))
words = word_tokenize(text)
freqTable = dict()
for word in words:
word = word.lower()
if word in stopWords:
word = stemmer.stem(word)
if word in freqTable:
freqTable[word] += 1
freqTable[word] = 1
sentences = sent_tokenize(text)
sentenceValue = dict()
for sentence in sentences:
for word, freq in freqTable.items():
if word in sentence.lower():
if sentence in sentenceValue:
sentenceValue[sentence] += freq
sentenceValue[sentence] = freq
sumValues = 0
for sentence in sentenceValue:
sumValues += sentenceValue[sentence]
# Average value of a sentence from original text
average = int(sumValues / len(sentenceValue))
summary = ''
for sentence in sentences:
if (sentence in sentenceValue) and (sentenceValue[sentence] > (1.2 * average)):
summary += " " + sentence
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