So in this lecture, we will be introducing the next section of this course, which is all about how
to build summaries out of text.
Now, as always, we're going to answer the question why is this practical?
And I hope that this is another one of those tasks where the answer is very obvious.
So why should we summarize text?
Well, notice how we already do this all the time.
For example, when you read a scientific paper, it always comes with an abstract and abstract is a
summary of that paper.
So you don't need to read the whole paper if you just want to understand the basic idea.
You can get away with just reading the abstract and then read the paper in full if you want more details.
Professional documents also have a similar concept, although instead of calling it an abstract, it's
sometimes called an executive summary.
So clearly, this is a useful concept in the real world since we do this already, even without the
help of computers.
And obviously, we can extend this concept to our own lives.
I know that for myself, my reading list is longer than the time I have just been reading, so it would
be very useful if I could reach the same outcome or reach the same conclusions by reading only the most
important parts of each document that I have to read.
Another reason we might want to build tech summarisation systems is for pushing forward the field of
artificial intelligence.
Now you might think this seems kind of trivial.
Build a algorithms to develop the field of A.I., but that's not what I mean.
What I mean is that tech summarization is kind of a critical aspect of learning.
For instance, think of everyday conversations that you have with other people.
Sometimes when people explain things to you, you will paraphrase or summarize what they've told you
in response.
Then the other person might say, Yes, you're correct or no, you're not correct.
So summarization is a way for learning systems, whether that's yourself or a machine, to demonstrate
their understanding of a concept.
If you think about it further, you'll realize that whenever you summarize an article or write an abstract,
it really requires you to have a good understanding of what you are trying to summarize.
In other words, it seems to require a true intelligence.
Here's another application for building summaries out of text.
In fact, you see this so often you probably don't even realize that it happens.
As you recall, our sort of window to the internet is search engines such as Google, Bing, DuckDuckGo
and so forth.
Now, without looking, think about what you see after you type in a query.
And while the answer is you see a bunch of search results and how are these search results presented?
Well, typically you have a webpage title, a link to the webpage and the corresponding summary, which
is these days usually customized your query.
So this is another very important application.
So now that you know why tech summarization is useful, let's discuss one detail before we outline this
section.
This detail is that we can actually categorize different methods of summarizing text.
These categories are extractive and obstructive.
So what do these mean?
Well, extract of summaries or summaries that are built out of the document itself?
So the summary is a subset of the document that was given.
In other words, the summary was extracted from the document.
Abstract of summaries, on the other hand, are novel sequences of text that are not just partial copies
of the given document.
As you can imagine, extractive summaries are much easier to generate compared with abstract of summaries
with extractive summaries.
We only need to identify the most relevant parts of the input, but with abstract of summaries, we
need to develop a deeper understanding of the input.
And we also need to convert that understanding into coherent text.
Note that this section of the course will focus on extractive summaries, abstract of summaries are
more suited to deep neural networks, such as sequence to sequence models and transformers.
So now that you understand our general approach to the SUMMARISATION task, let's look at a section
outline.
This section will look at two methods.
The first is pretty simple and only requires knowledge you learned in the first part of this course
on vector based methods.
The second is more complex and builds on the first.
This one is called Tex Rank, which is based on Google's Page Rank.
So if you've learned about Page Rank in the past, then this should be familiar.
This one is more advanced and requires a bit of knowledge about probability and linear algebra, including
Markov chains, although the way we use Markov chains is different from the probability section of this
course.
Note that if you were a beginner student and you only wants to know how to use text rank without going
through any advanced math, this is possible after the advanced lectures will have one more beginner
lecture where we look at libraries that implement text rank, which you can use directly.