Text summarization is the process of generating summaries from a given long text. Based on the Luhn work, The Automatic Creation of Literature Abstracts (1958), a naïve summarization approach known as NaiveSumm is developed. It makes use of a word's frequencies for the computation and extraction of sentences that consist of the most frequent words. Using this approach, text summarization can be performed by extracting a few specific sentences.
Let's see the following code in NLTK that can be used for performing text summarization:
from nltk.tokenize import sent_tokenize,word_tokenize
from nltk.corpus import stopwords
from collections import defaultdict
from string import punctuation
from heapq import nlargest
class Summarize_Frequency:
def __init__(self, cut_min=0.2, cut_max=0.8):
"""
Initilize the text summarizer.
Words that have a frequency term lower than cut_min
or higer than cut_max will be ignored.
"""
self._cut_min = cut_min
self...