Hey, everyone.
So next thing which we are going to do is sentence tokenization.
So let me just segment all those sentences and then we'll try to find those caudal individual sentence
based on this word frequency count.
We have created.
So let's say for St..
So this thing we have already done in this sentence segmentation review, that will be a sense and say
so there will be a list comprehension.
I hope take.
And every single sentences I will carry has a list of sentence.
So I can see, like, it's a san.
Underscore, let's say, tokens.
Hey, let me bring
centredness tokens.
All right, so individuals sentence.
You can see he's a part of one single values.
No, now a three do it every single sentences.
And we will try to provide a score for every single sentence.
And for that, let's create the same way, like a dictionary.
So it will be a sentence score, let's say score and one empty dictionary.
And this dictionary will wilhoit the individual sentences as a key and its value corresponding to those
sentence scores.
And in the next video, the next processing on words, we will try to grab all those 30 percent digital
maximum sentence score.
Holly, let me executive.
And first, Lexi cradle what every single sentence so far in the same sentence broken.
We are going to recreate everything that even a word.
For Vought in.
Sank.
And let us print all those for.
Plus the hot.
So these are all words, no individual sentences.
All right.
Next thing is based on individual verse, frequency, counter.
We are going to go to school to individual sentences.
So now for each word.
First of all, we will try to find whether those were appears in a dictionary or not.
There is a 100 percent chance that it will appear because we'll just grab this thing from the same article.
Only it may happen that this automated text summarisation you will be built upon, let's say, million
different tactical like for the treaty.
And when you do such a kind of testing, you will encounter some different article.
And that's why this many checks, we are going to do it for our kids.
It is not required because we are doing training and you can say kind of testing or kind of text summarization
automated.
We on the same article.
So obviously all those were you will definitely appear in this word frequency.
Counter that toward North Texas, lower in frequency.
Your keys, if it appears inside the keys, then only real Kaposi.
Hey.
Because in that case, only Velho discord associated with individual would.
Now, let's first grab if that sentence up here in the sentence or not.
So if, say, in sentence score.
Not peace.
So the first time when some sentences has not been assigned any score, those particular, he definitely
doesn't exist.
And if those particular sentence already appears in the sentence, quote, it may be assigned some school.
And in that case, we are just going to update those code based on different was.
So instead of sentencing, we are going to foster.
Look, if sentence is not appear so fast, partition.
We will do like this and we are going to use this sentence scor sentence.
So individual sentence inside this sentence called.
We will give some weight, each of those weight age will be associated with void.
So that will be a move.
And those particular words value school.
We will get it from our frequency.
So from our frequency, we will get those score.
Let just make it tax, not lower.
All right.
And suppose he already appears in sight that we are just going to update it so as.
Let me selective,
let me put here plus.
So if all those sanctions appear in a sentence court.
In that case, it is just going to update those sentenced based on what they would appear.
And for that quote, what is the schools associated with?
Let me add only and it will fill up this sentence, quote, lectionary for us.
All right, so then let's try to display sentence quote.
Let me bring Kate.
All right.
So you can see the first ever sentence Maria Sharapova has physically.
It has its codes like zero point eight three three eight.
All right.
Same way like four seconds sentence.
The score is one point lately.
Now, what we are going to do will try to grab all the score, having the maximum value.
So first 30 percentage of all the sentences having a maximum value.
And those particular sentence will just combine.
And we will assign it as a summary of all.
It wasn't exciting.
So getting those thirty percent is maximum scoring sentence.
We will see in the next 12.