Use case – Auto-summarize : Natural Language Processing (NLP) using Python NLTK




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Using Python NLTK to perform rule-based natural language processing for auto-summarize use case. It’s a practical based video do watch and share the feedback.

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  • Vinil Vadakkepurakkal
    November 23, 2020

    I have a small code addition that will help attain better results:
    After you get word_sent using word_sent = [word for word in word_sent if word not in stopword]
    The word_sent you get have a lot of tokens that are one character in length such as ", ', -, et cetera. Thus, the algorithm in the video prefers paragraphs that have lots of quotes, which aren't always the best summaries. Here is the code to fix this:
    temp = []
    for i in word_sent:
    if len(i) > 1:
    temp.append(i)
    word_sent = temp

    This code removes any word_sent tokens of length one, which are almost always nonsensical anyway. This helps give better results with the actual best summaries, not just the ones with lots of length-one tokens!

    Great video otherwise, thanks a lot!!

  • Vinil Vadakkepurakkal
    November 23, 2020

    Great

  • Vinil Vadakkepurakkal
    November 23, 2020

    Hi Vinil, it’s really great to see the power of Python.; however one suggestion. This video would be even better had it had a good flow e.g. 1.problem statement (huge text to read) 2. Show the out come in the beginning that is summary used text. 3. Explain the requirements to achieve this. 4. Walk us through the procedure. This way this would be more interesting… I hope you will keep sharing such interesting videos. Keep rocking! My best wishes 👍👍👍👍👍

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