Text Summarization with NLTK in PythonUse Jupiter notebook, label question, code and screenshot Natural Language Processing (NLP) has a subfield called text summarization that works with...

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Text Summarization with NLTK in Python Use Jupiter notebook, label question, code and screenshot Natural Language Processing (NLP) has a subfield called text summarization that works with extracting summaries from massive amounts of text. NLP-based approaches and deep learning-based techniques are the two primary categories of text summarizing techniques. This article will demonstrate a straightforward NLP-based text summarizing method. In this essay, we won't employ any machine learning libraries. Instead, we will only utilize the NLTK library in Python to summarize Wikipedia articles. Getting Wikipedia Articles We first need to fetch Wikipedia articles from the internet before we can summarize them. We'll make use of a few libraries to accomplish this. The first library that has to be downloaded is the excellent Python web scraping tool called delicious soup. https://realpython.com/beautiful-soup-web-scraper-python/ Execute the following command at the command prompt to download the Beautiful Soup utility. pip install beautifulsoup4 pip install lxml The most popular platform for creating Python programs that use human language data is called NLTK. Along with a collection of text processing libraries for categorization, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum, it offers simple interfaces to more than 50 corpora and lexical resources, including WordNet. pip install nltk You should implement the following: · Preprocessing · Removing Square Brackets and Extra Spaces · Removing special characters and digits · Converting Text To Sentences · Find Weighted Frequency of Occurrence · Calculating Sentence Scores · Getting the Summary Welcome to added more info in Now, we have a dictionary called sentence scores that lists sentences along with their related scores. We can use the top N sentences that received the greatest scores to summarize the article. Your script should prints the top seven sentences from the database. And print some data graph Your submission should include the Jupyter notebook scripts and a screenshot for the outputs.