Hey, everyone.
So next Project Cough and LP, which we're going to work upon, is a tax simulation and for illustration.
But it was kept here one big tax.
So what is tax summarization is.
So we all know that we are all bombarded with lots of lots of data.
There is lots of articles we keep on reading on a daily basis.
So there are lots of data around us.
Mostly in a tax forms.
I won't say all because I in a tax form.
But many times we keep on releasing a tax forum.
And it consumes lots of lots of a long time.
It may happen that there will be, let's say, 5000 words article.
And maybe the important point will be just five or 10 important points today.
So even if you just can perhaps somehow those five important points, that will be sufficient for you
to grasp those whole article.
So the idea behind this automatic tax symbolizes and is the same that whether you can pick up all those
important 10 important statement on a sentence or some kind of somebody out of it, can you grab it
through automatic this natural language processing related techniques?
And that is what the idea behind this tax summarizes.
Something like a finding all those useful information out of huge amount of tax.
And even it will reduce your reading time also.
So you can even possess more and more documents.
So you can be very much choosy in choosing or selecting some kinds of article.
Because now you can process more and more article while reading.
So mainly these are some of the big advantage of this tax summarizes an application.
And as I told you, I already created this particular tax so you can see Maria set up for a related
one article, is that so?
I would highly suggest to you that you just pause this video and read this whole article, which I kept
putting a factual string.
Let me executive and let's see how many characters are there inside this.
So you can see we have fifteen hundred sixty three characters out there inside this particular stream.
So instead of writing such a huge amount of text, can we just come up with some limited set of sentences
or words like a hundred maybe or maybe 200?
And that will just easily summarize it.
So, as I told you, just pause this video and read this full article.
So when we find a summarization of this particular article, you'll get to know about.
How good of a summarization is because as a human, you can create those somebody easily, as far as
you can understand, those cyclical tax or any huge amount of tax you are trading on.
All right.
So let's proceed ahead with importing this minimum library.
And then we'll see what techniques and mechanism we are going to apply to choose all those important
information out of this article.
So let me create.
Let's import less Pesi Lively
and Fromm's Tracy.
Not Lange, not even.
Dutch Topo's We are going to import stop.
And one more thing from this ruling class, we are going to import increased punctuation.
All right.
Let me.
Security.
Let's load our small size, Martin.
So it will be NLB, spacey, dark lord.
An endless court.
Court.
And this put a verb in this quote, test him.
Let me run.
Let's apply this whole tax honor and be model so far that we are going to use this in L.P tax.
Let me assign it to some dark object.
And legislate played over every single token between will cook in.
No, let's bring them to conduct tax.
And let us keep it as a list comprehensive.
So that will be netting what tokens?
Let me print pawprints.
All right.
So these are the tokens with which we are going to work up on.
All right, so one more thing is if you just display this punctuation.
So these are all contrition marks.
Now, what we are going to do, we are going to add one more contrition that will be slashed.
And so new line and let me.
Assigning to same contrition.
And if you displayed.
You can see now less and also exist as a punctuation mark.
All right, so deserted some of the minimum stuff while reading your actual article, which we are going
to use for the somebody purpose.
All right.
So there is a first step.
Hopes.
I kept here the first Luol heading.
Let me make it here.
So firstly, 130.
Next, is tax planning really do and then sentenced tokenization.
And before that?
Let me discuss what idea on which we are going to create this summarization.
So there is a one basic technique which we can apply, like we can try to do the score of individual
sentence so we can do the sentence tokenization.
And to each particular, let's say, sentence, we will give us some score and we can try to find out.
The highest court or first, let's say 20 or 30 percentage of those standards, and that will be nothing
but a tax summarization.
But now the question is how to view those scores to individual sentence for that.
There is a one basic idea you can apply, like we can first create a word frequency.
And those frequency will give us the information that how many times each individual vocabulary word
here appears.
And based on that, we can give some score to individual words in a sentence and score.
We can just add it up according to individual voice appeared in a sentence.
So as we process along, you will get idea in sooner text leaning part in a sentence, tokenization.
In the next video, we will see how to achieve those things.
First, we'll try to create those word frequency contact.
All right.
So see you in the next video.