There’s so much text in today’s world, pretty much none of us have
enough time to read all the things we wish we had time to. So one
of the most exciting applications I’ve seen of
large language models is to use it to
summarise text. And this is something that I’m seeing multiple teams
build into multiple software applications. You can do this
in the Chat GPT Web Interface. I do this all
the time to summarise articles so I can just kind of read the content of many
more articles than I previously could. And if
you want to do this more programmatically, you’ll see how to
in this lesson. So with that, let’s dig into the code to
see how you could use this yourself to summarise text.
So let’s start off with the same starter code as you saw
before of importOpenAI, load the API key and here’s that
getCompletion helper function.
I’m going to use as the running example, the
task of summarising this product review. Got
this panda plush toy from a daughter’s birthday
who loves it and takes it everywhere and so on
and so on. If you’re building an e-commerce website
and there’s just a large volume of reviews, having
a tool to summarise the lengthy reviews could
give you a way to very quickly glance
over more reviews to get a better sense of what all your
customers are thinking. So here’s a
prompt for generating a summary. Your task is to generate a
short summary of a product review from e-commerce websites, summarise
the review below and so on in at
most 30 words.
And so this is soft and cute panda plush toy loved by
a daughter but small to the price, arrived early. Not bad, it’s
a pretty good summary. And as you saw in the previous video, you
can also play with things like controlling the character
count or the number of sentences to affect the length of this
summary. Now, sometimes when creating a summary, if
you have a very specific purpose in mind
for the summary, for example, if you want to give feedback
to the shipping department, you can also modify the prompt to
reflect that so that it can generate a summary that is more
applicable to one particular group in
your business. So, for example, if I add to give feedback
let’s say I change this to start to focus on
any aspects that mention.
shipping and delivery of the product. And if I run this, then
again, you get a summary, but instead of starting
off with Soft and Cute Panda Plush Toy,
it now focuses on the fact that it arrived a day earlier
than expected. And then it still has, you know, other details. Or
as another example, if we aren’t trying to give feedback
to the shipping department, but let’s say we want to give feedback
to the pricing department.
So the pricing department is
responsible for determining the price of the product.
I’m going to tell it to focus on
any aspects that are relevant to the price and perceived value.
Then this generates a different summary
that says maybe the price may be too high for its size. Now,
in the summaries that I’ve generated for the
shipping department or the pricing department, it
focuses a bit more on information relevant to
those specific departments. And in fact, feel free to pause
the video now and maybe ask it to generate information for the
product department responsible for the customer
experience of the product.
Or for something else that you think might
be related to an e-commerce site.
But in these summaries, even though it
generated the information relevant to shipping,
it had some other information too, which you could decide may
or may not be hopeful.
So depending on how you want to summarize it,
you can also ask it to extract information
rather than summarize it. So here’s a prompt that says you’re tasked
to extract relevant information to give
feedback to the shipping department. And now it just says
product arrived the day earlier than expected without all
of the other information, which was
also hopeful in the general summary, but less
specific to the shipping department if all it wants to know is
what happened with the shipping.
Lastly, let me just share with you a concrete
example for how to use this in a workflow to help summarize
multiple reviews to make them easier to read.
So, here are a few reviews. This is kind of long, but you know,
here’s the second review for a standing lamp, needle
lamp on the bedroom. Here’s the third review for an
electric toothbrush. My dental hygienist recommended it. Kind of
a long review about an electric toothbrush. This is
a review for a blender when they said, so, so that
17 piece system on seasonal sale and so
on and so on. This is actually a lot of text. If you
want, feel free to pause the video and read through all
this text. But what if you want to know what these reviewers
wrote without having to stop and read all this in detail. So
I’m going to set review 1
to be just the product review that we had up there. And
I’m going to put all of these reviews into a list. And
now if I implement a
for loop over the reviews.
So here’s my prompt and here I’ve asked it to summarize it in
at most 20 words. Then let’s have it
get the response and print it out. And let’s run that.
And it prints out the first review was that Pantatoi
review, summary review of the lamp, summary review of the toothbrush,
and then the blender.
And so if you have
a website where you have hundreds of reviews,
you can imagine how you might use this
to build a dashboard to take huge numbers of reviews,
generate short summaries of them so that
you or someone else can browse the reviews much more quickly.
And then if they wish, maybe click in to
see the original longer review. And this can help
you efficiently get a better sense of what
all of your customers are thinking.
Right. So that’s it for summarizing. And I hope that you can picture if you
have any applications with many pieces of text, how
you can use prompts like these to summarize
them to help people quickly get a sense of what’s in
the text, the many pieces of text, and perhaps
optionally dig in more if they wish.
In the next video, we’ll look at another capability
of large language models, which is to make inferences using text. For
example, what if you had, again, product reviews and you
wanted to very quickly get a sense of which product reviews have
a positive or a negative sentiment? Let’s take a look at how to do
that in the next video.