All right, anyway, so let's try to grab plus 30 percentage of sentences, which is having a maximum
score out of this sentence code dictionary we helped create.
So for that, we are going to use this from Tipu.
Import and largest.
Let's first grab how many total sentences that that sentence caught hiding lengthens function will work
on a dictionary or so.
So that will be a 24th.
And if we just grab zero point three of that, that will be a.
So point nineteen.
That means maximum eight sentences.
We can grab it.
So let's try to grab first a sentence.
So let's use and the largest.
So the first argument, we have to pass it.
And that how many of them you want to.
Selective.
Let's just go with eight, which is terrible, on which you are going to do all those things.
So any terrible will be sentenced.
Court.
Let me make it here.
And it's great and based on which key you are going to do all those things.
So he will be sentenced.
He thought you get.
Now, here you are not calling any function instead of the get function will return as those value.
And let me assign it to somebody.
Let me bring somebody.
So these are the.
Summarization of our own article.
So first, let's try to combine all those sentences together and we will put it into one single bucket.
So far they were not tax.
Far more in somebody's.
And that will be a sign and underscore somebody.
Let me bring Dick.
And that will be a part of.
Now we can even simple use like a joint space on this final somebody.
And let me just misplay.
So that is our complete final somebody.
Let me make it into radio.
Let's hear somebody tell.
Let me bring
somebody video, but.
All right.
So that is the summarization of the whole article.
Now we can do anything, we can just simply find a land of this somebody.
That will be a six.
Thirty two characters.
And if you just try to divide this thing by our holy text.
So that will be a.
It's almost 40 percent is your data.
We gather as a summarization.
Almost 60 percent range of data to reduce.
Really?
So that is what the summary of our whole article.
But this is just a very simple mechanism.
We have applied for automated text summarization.
All right.
So that is the whole idea behind the text summarization.
See you in the next video.
Will see some more stuff related to natural language processing.