Text summarization is a very useful and important part of Natural Language Processing (NLP). First let us talk about what text summarization is. Suppose we have too many lines of text data in any form, such as from articles or magazines or on social media. We have time scarcity so we want only a nutshell report of that text. We can summarize our text in a few lines by removing unimportant text and converting the same text into smaller semantic text form.
1. Python3
2. Qt creator (Just build and run the .pro file inside)
includes:
1.Pre-Processing
2.Adding Cue phrases
3.Numeric Data
4.Sentence Length
5.Sentence Position
6.Upper Case Letters:
7.Proper Noun
8.Noise removal (emails,questions,urls)
9.Named Entities
10.Prioritising Dates
11.Implementing Bag Of Words , tf, idf and tf-idf
12.Calculating Total Score of a sentence and adding it to summary
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