
On “Learning to Summarize” - luu
https://nostalgebraist.tumblr.com/post/629020418641199104/on-learning-to-summarize
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simonebrunozzi
I initially thought this would be about "humans" learning to summarize longer
texts in a smart way. It's a topic dear to my heart; in fact, I even launched
a little side project a few years back, called MNMN [0], with the aim of
providing summarization of interesting articles or discussions on the web. I
started with my own itch to scratch: Hacker News homepage, which I religiously
read pretty much every day.

Here's an example of how the Hacker News home page would be summarized: [1].

Instead, this "learning to summarize" article is about another interesting
topic, which is how you teach an AI (GPT-2 and GPT-3) how to summarize text.
It's also - bear with me - something dear to my heart, as I used to teach
compilers at a CS course in an Italian university in 2004-2006, and I
developed an interest for languages in general (not just computer ones).

This one below is the central point of the article, and it is indeed a crucial
part of having success with an AI (note: LM stands for Language Model):

> IMO there are two almost unrelated ideas going on in OpenAI’s preference
> learning work.

> First, the idea of collecting binary preference annotations on LM samples,
> and (in some way) tuning the LM so its samples are better aligned with the
> preferences.

> Second, a specific method for tuning the sampling behavior of LMs to
> maximize an (arbitrary) score function defined over entire samples.

We are, IMHO, at the cusp of a true revolution in linguistics. Can't wait to
see what happens in the coming 18-24 months. I expect to be blown away on at
least a few fronts.

[0]:
[https://github.com/simonebrunozzi/MNMN](https://github.com/simonebrunozzi/MNMN)

[1]: [https://github.com/simonebrunozzi/MNMN/blob/master/Weekly-
Su...](https://github.com/simonebrunozzi/MNMN/blob/master/Weekly-
Summaries/2016-10.md)

~~~
DenisM
How much traction/revenue would you need to keep mnmn alive? Like, if you made
$1000 per day you would probably keep it up?

~~~
Zakuzaa
$1000 per day?

~~~
DenisM
Yes, that amounts to $250,000 year.

~~~
Zakuzaa
Hmm doesn't sound that much now that I think.

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binarymax
It’s interesting that the title seems like a mix on Learning to Rank, in which
a preferred ordering is learned for relevant ranking of search results...but
doesn’t mention this anywhere in the article! As noted, you don’t want to do
RL if you can avoid it - so I’m wondering if learning decision trees (common
in LTR with LambdaMART) can be helpful with summarization to best fit the
annotator preferred passages over others. Perhaps the title name is just a
coincidence but there is value in exploring, for example, random
forests...which are way easier (and faster) than things like RL or CNNs

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supernova87a
Wow, that article had nothing to do with the title, for a normal person
reading it.

It should really get a much more technical title, befitting the dissertation
it leads to.

