Summarize text using nltk and numpy

summarizer.py

import nltk
import numpy as np
from nltk.stem import PorterStemmer
from nltk.text import TextCollection
from nltk.corpus import stopwords as sw
from nltk.tokenize import word_tokenize, sent_tokenize
stem = PorterStemmer().stem
nltk.download('stopwords')
stopWords = sw.words('english')
normalize = lambda array: np.divide(array, np.max(array))
def RankText(text):
	"Function to rank the sentences in the text based on their importance"
	sentenceCollection = TextCollection(tuple(map(# Create a TextCollection object to work with sentences
		lambda sentence: [
			stem(word)
			for word in word_tokenize(sentence.lower())
			if (word.isalpha() and word not in stopWords)
		],
		sentences := sent_tokenize(text)
	)))
	RankedWords = tuple(zip(# Rank words based on their importance using TF-IDF
			uniqueWords := set(sentenceCollection.tokens),
			normalize(tuple(map(sentenceCollection.idf, uniqueWords)))
	))
	return (
		tuple(zip(# Rank sentences based on the importance of words they contain
			sentences,  # Current sentence
			normalize(np.sum([
				np.divide([
						rank
						if word in sentence
						else 0
						for word, rank in RankedWords
					],
					float(len(sentence)) # Avoid long sentence bias 
				)
				if len(sentence) != 0  # Avoid division by zero
				else np.zeros(len(RankedWords))  # Create a zero vector for empty sentences
				for sentence in sentenceCollection._texts
			],
				axis=1 # Sum of each row
			))
		)), 
		dict(RankedWords)
	)
def getAboveAvg(Sranks):
	"Function to get sentences above the average importance"
	avg = sum(map(lambda i: i[1], Sranks)) / len(Sranks)
	return tuple(filter(lambda i: i[1] > avg, Sranks))
Sranks, Wranks = RankText(open('sample.txt').read())
print("".join(map(lambda i:i[0],getAboveAvg(getAboveAvg(Sranks)))))
print(Wranks)