Summarize Twitter Live data using Pretrained NLP models
Introduction
Twitter users spend an average of 4 minutes on social media Twitter. On an average of 1 minute, they read the same stuff. It shows that users spend around 25% of their time reading the same stuff.
Also, most of the tweets will not appear on your dashboard. You may get to know the trending topics, but you miss not trending topics. In trending topics, you might only read the top 5 tweets and their comments.
So, what are you going to do to avoid wastage of time on Twitter?
I would say summarize your whole trending Twitter tags data. And, then you can finish reading all trending tweets in less than 2 minutes.
In this article, I will explain to you how you can leverage Natural Language Processing (NLP) pre-trained models to summarize twitter posts based on hashtags. We will use 4 ( T5, BART, GPT-2, XLNet) pre-trained models for this job.
Why use 4 types of pre-trained models for summarization?
Each pre-trained model has