Summarize paragraph4/12/2023 text = re.sub(r'\*\]',' ',text) # replace one or more spaces with single space text = re.sub(r'\s ',' ',text) text = file_data # replace reference number with empty space, if any. ,, , with empty space (if any…), (B) replace one or more spaces with single space. We will (A) replace reference number, i.e. Here, we use regular expression to do text preprocessing. # load text file with open('Apple_Acquires_AI_Startup.txt', 'r') as f: file_data = f.read() The goal here is to have a clean text that we can feed into our model. (3) Import text and perform preprocessing (2) Import libraries # Natural Language Tool Kit (NLTK) import nltk nltk.download('stopwords') nltk.download('punkt') # Regular Expression for text preprocessing import re # Heap (priority) queue algorithm to get the top sentences import heapq # NumPy for numerical computing import numpy as np # pandas for creating DataFrames import pandas as pd # matplotlib for plot from matplotlib import pyplot as plt %matplotlib inline You can also download the text document from my Github. You can find the original news article here. I used the text from a news article entitled Apple Acquires AI Startup For $50 Million To Advance Its Apps. Import text> clean text and split into sentences > remove stop words > build word histogram> rank sentences> select top N sentences for summary Text Summarization Workflowīelow is the workflow that we will be following… The benefit of doing this is that you don’t need to train your model to use it for your document. We will use word histogram to rank the importance of sentences and, subsequently, create a summary. I use extractive summary because I can apply this method to many documents without having to do a lot of (daunting) machine learning model training tasks.īesides that, extractive summarization gives better summary outcome than abstractive summary, because abstractive summarization has to generate new sentences from the original text, which is a more difficult method than a data-driven approach to extract important sentences. Which summarization method should I use, and why? Extractive summary > recognize important sentences and create a summary using those sentences.Abstractive summary > generate new sentences from original text.There are two general types of summarization: It’s basically a task to generate an accurate summary while maintaining key information and not losing overall meaning. In this story, I will show you how you can create your personal text summarizer using Natural Language Processing (NLP) in Python.įoreword: Personal text summarizer is not hard to create - a beginner can easily do it! What is text summarization? Summarization has become a very helpful way of tackling the issue of data overburden in the 21st century. Have you ever had one too many reports to read and you just want a quick summary of each report? Were you ever in a situation where everybody just wanted to read a summary instead of a full-blown report? A guide to creating your personal text summarizer
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