![]() $ pip install beautifulsoup4Īnother important library that we need to parse XML and HTML is the lxml library. Execute the following command at the command prompt to download the Beautiful Soup utility. The first library that we need to download is the beautiful soup which is very useful Python utility for web scraping. To do so we will use a couple of libraries. Fetching Articles from Wikipediaīefore we could summarize Wikipedia articles, we need to fetch them from the web. In this section, we will use Python's NLTK library to summarize a Wikipedia article. Now we know how the process of text summarization works using a very simple NLP technique. These two sentences give a pretty good summarization of what was said in the paragraph. Ease is a greater threat to progress than hardship. So, keep moving, keep growing, keep learning. Similarly, you can add the sentence with the second highest sum of weighted frequencies to have a more informative summary. You can easily judge that what the paragraph is all about. For instance, look at the sentence with the highest sum of weighted frequencies: The sentences with highest frequencies summarize the text. The final step is to sort the sentences in inverse order of their sum. Sort Sentences in Descending Order of Sum So, keep moving, keep growing, keep learning It is important to mention that weighted frequency for the words removed during preprocessing (stop words, punctuation, digits etc.) will be zero and therefore is not required to be added, as mentioned below: SentenceĮase is a greater threat to progress than hardship The final step is to plug the weighted frequency in place of the corresponding words in original sentences and finding their sum. Replace Words by Weighted Frequency in Original Sentences Since the word "keep" has the highest frequency of 5, therefore the weighted frequency of all the words have been calculated by dividing their number of occurances by 5. The following table contains the weighted frequencies for each word: Word We can find the weighted frequency of each word by dividing its frequency by the frequency of the most occurring word. Next we need to find the weighted frequency of occurrences of all the words. ![]() After tokenizing the sentences, we get list of following words: ['keep', We need to tokenize all the sentences to get all the words that exist in the sentences. After preprocessing, we get the following sentences:
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