So in this lecture, we will summarize everything we learned in this section.
This section was all about how to summarize text.
We learned about two different approaches to this problem, specifically extractive and obstructive.
To review extractive is when the summary is made up of pieces of the given documents.
On the other hand, obstructive is when the summary is made up of novel text more like a paraphrasing
of the input.
Clearly, extractive summarization is easier because it only amounts to choosing which sentences are
the most relevant.
On the other hand, abstract of summarization is hard because it requires us to generate text, which
even by itself is pretty difficult.
Abstracted methods are more suited to deep learning models such as seek to seek in transformers.
This section looked at two methods of summarizing text where the second builds upon the first.
The first method was pretty simple.
It only involved using the techniques we learned from the first part of this course, which was on vector
based methods, in particular TFI Taf.
The basic idea was we would compute a tier free of matrix from a document split in two sentences.
We could then score each sentence using the average of the non-zero tier free of values.
From there, we simply sought each sentence by their corresponding score.
In return, the top scoring sentences.
The second method we looked at was called Tex Rank, and it gave us a better way of scoring each sentence.
Most of the steps remain the same, which was convenient.
The Tex Rank method is based on Google's Page Rank, which treats every web page as a state in a Markov
chain.
The Page Rank score is then just the probability you would land on a specific webpage after doing a
random walk for an infinite number of steps.
If you looked at the advanced lectures, you know the conditions under which such a score exists and
a simple way for us to ensure that those conditions are true.
Importantly, you also learned an efficient way to compute these scores.
For those who prefer a more beginner approach, we also looked at a few libraries that give you back
a summary in just a few lines of code.
Those libraries included text rank, among other methods.
Finally, recognize that both of the methods we learned about compute, the summary based only on the
document we wanted to summarize.
In other words, unlike other machine learning methods, it doesn't require us to train on a whole text
corpus, learn a language model and so forth.
If we wanted to build an abstract of summariser, that would be required since we need a language model
in order to know how to generate text using this method, we only need the document itself, which is
simpler and more efficient.
So hopefully you found this section useful both for learning more about NLP and for reducing your own
reading time.
If you're like me and you have a lot to read.
Thanks for listening, and I'll see you in the next lecture.