Last Updated on November 7, 2021 by shibatau
Learning NLP
There are two approaches to summarizing texts in Natural Language Processing, extraction and abstraction.
In extraction-based summarization, a subset of words that represent the most important points are extracted from texts and combined to make a summary.
Here we will learn extraction-based summarization from the following posts.
1.Building a text summarizer in Python using NLTK and scikit-learn class TfidfVectorizer
2.A Gentle Introduction to Text Summarization in Machine Learning
1.Extraction_based summarization 1
Show the difference between the original document and the summarized one using Google Docs.
You can compare texts online:
https://countwordsfree.com/comparetexts
Scripts
I have run the scripts in the above post on Google Colaboratory. You need to upload two files called “ai.txt” beforehand.
https://colab.research.google.com/drive/1rB_PssCJ21DCWKJhcyDVz9sfBv9XVNF6?usp=sharing
2.Extraction_based summarization 2
I have run the scripts on Google Colaboratory:
https://colab.research.google.com/drive/1aUKCgCvodKS-FYCIMzinQI3SyOKykfdy?usp=sharing