I agree that formatting abstracts as single paragraph blocks is surprisingly bad for comprehension; I think it is because abstracts are deceptively difficult for the reader, as they tend to invoke a lot of extremely novel & unusual keywords/concepts and make new claims within the space of a few sentences (not infrequently dumping in many numbers & statistical results into parentheticals, which might have a dozen stats in less space than this), and that they are deceptively easy for the authors to read because they suffer from the curse of expertise. Once the reader has paid the cognitive tax of recalling and organizing all the concepts, then suddenly the abstract stops being so confusing.

Introspecting the experience, it feels as if the lack of explicit keywords like 'Results:' or their equivalent paragraph-breaks, is 'the straw that breaks the camel's back'. It's not that it is inherently difficult to understand a single run-on paragraph, it's that it is an extra burden at the worst possible time. (The same run-on paragraph would be read effortlessly a few paragraphs later, after much of the terminology has been introduced.)

I have sometimes tried to read a single-paragraph abstract, found my eyes glazing over as I lose track of the topic amidst the flurry of jargon (is this sentence part of the intro or is it methodology or...), and have to force myself back to the start, read it sentence by sentence, and wait for my understanding to catch up, at which point the abstract suddenly makes sense and I feel a bit frustrated with myself. (As a generalist, I read all sorts of abstracts and have to pay the 'abstract tax' each time, so I've been sensitized to the ways in which, say, CS & math abstracts tend to be much harder to read than explicitly-standardized keyworded medical abstracts reporting a clinical trial, and machine learning abstracts intermediate because they usually follow the standard organization but without keyword markers.)

This is also why it is so painful to read a series of 1-paragraph abstracts: you are being slammed in the face repeatedly by ultra-dense prose which rubs salt into the wounds by removing the typographical affordances you have been trained to expect.

What I do on Gwern.net is:

1. use a large set of regexp rewrites to try to reformat keyword-delimited abstracts into a consistent set of keywords. Every journal seems to have its own twist on the standard format of Introduction/Methods/Results/Conclusion, and they all suck and are made worse for the inconsistencies.
2. wrote a simple paragraphizer.py GPT-3 API script which runs automatically on new annotations: if there are no newlines in the abstract, it calls the API with the abstract, asks it for a newline-split version, and if the new version with newlines removed== old version, returns it. It often fails, and I'm not sure why, because the task seems semantically quite simple. Probably the prompt is bad or I don't use enough shots.
3. Deliberately add newlines to all abstracts I annotate by hand, sometimes rearranging the abstract to fit the standard format better.
4. Have a lint check for abstracts which detects if they lack newlines (not quite as easy as it sounds since it's all in HTML, so you have to take into account that it's not as simple as merely detecting whether there's more than one p element - lists, blockquotes, tables etc), and prints out a warning so I will go and manually insert newlines.

I always find the processed versions to be much more readable than the originals, and I hope it helps readers navigating a sea of references.

I'm one of the authors on the natural abstractions review you discuss and FWIW I basically agree with everything you say here. Thanks for the feedback!

We've shortened our abstract now:

We distill John Wentworth’s Natural Abstractions agenda by summarizing its key claims: the Natural Abstraction Hypothesis—many cognitive systems learn to use similar abstractions—and the Redundant Information Hypothesis—a particular mathematical description of natural abstractions. We also formalize proofs for several of its theoretical results. Finally, we critique the agenda’s progress to date, alignment relevance, and current research methodology.

At 62 words, it's still a bit longer than your final short version but almost 3x shorter than our original version.

Also want to highlight that I strongly agree having TL;DRs at all is good. (Or Intros were the first 1-2 paragraphs are a good TL;DR, like in your post here).

Papers typically have ginormous abstracts that should actually broken into multiple paragraphs. 

I suspect you think this because papers are generally written for a specialist audience in mind. I skim many abstracts in my field a day to keep up to date with literature, and I think they're quite readable even though many are a couple hundred words long. This is because generally speaking authors are just matter-of-factly saying what they did and what they found; if you don't get tripped up on jargon there's really nothing difficult to comprehend. If anything, your 69 word version reads more like a typical abstract I see day-to-day than the more verbose version you had earlier; way too much filler to be a good abstract. For example, sentences like these ones rarely show up in abstracts:

This post summarizes and reviews the key claims of said agenda, its relationship to prior work, as well as its results to date. Our hope is to make it easier for newcomers to get up to speed on natural abstractions, as well as to spur a discussion about future research priorities.

Or, put more bluntly, papers really just aren't textbooks or press articles. They are written to be understandable to specialists in the field, and maybe adjacent fields (a PRL paper would be written to address all physicists, for example), but there's simply no effort made towards making them easy to understand for others. Look at what I consider to be a fairly typical abstract: https://arxiv.org/abs/2101.05078

It's really just 'We designed A. It works like this. We describe A and associated subsystems in detail in the paper. We characterise A by doing B, C, D, and E. The performance agrees with simulation." There are bad abstracts everywhere, of course, but I disagree that they're the norm. Many abstracts are quite reasonable, and effectively just say 'Here's what we did, and here's what we found'.

When I wrote my thesis my abstract was broken into 4 paragraphs. The examiners suggested making it all one paragraph because "an abstract should be just one paragraph". But the university template required the abstract to have a page to itself, and I thought the page breaks helped so kept them. Arguably the abstract could have been shorter, but for a thesis like document its harder, because a thesis (in practice) is kind of a mash of different things you did over several years crammed together, so it doesn't have "a main point".

I would add an option to use GPT-4 API to show a post summary, offloading it from a human. For the above abstract the bot suggests the following:

The text is about John Wentworth's Natural Abstraction agenda. This is an effort to understand and recover natural abstractions in realistic environments. The post provides a summary and review of the agenda, including its relationship to prior work and results. The goal is to help people understand natural abstractions and discuss future research priorities.

The post summarizes the intuition behind the agenda and relates it to previous work in various fields. It then lists key claims, including the Natural Abstraction Hypothesis and redundant information abstractions. The post also includes mathematical proofs for some of the key results in the redundant information abstraction line of work.

However, the post also critiques the agenda and its progress to date. It notes gaps in the theoretical framework and challenges its relevance to alignment. Additionally, it critiques John's current research methodology.

IMO ~170 words is a decent length for a well-written abstract (well maybe ~150 is better), and the problem is that abstracts are often badly written. Steve Easterbrook has a great guide on writing scientific abstracts; here's his example template which I think flows nicely:

(1) In widgetology, it’s long been understood that you have to glomp the widgets before you can squiffle them. (2) But there is still no known general method to determine when they’ve been sufficiently glomped. (3) The literature describes several specialist techniques that measure how wizzled or how whomped the widgets have become during glomping, but all of these involve slowing down the glomping, and thus risking a fracturing of the widgets. (4) In this thesis, we introduce a new glomping technique, which we call googa-glomping, that allows direct measurement of whifflization, a superior metric for assessing squiffle-readiness. (5) We describe a series of experiments on each of the five major types of widget, and show that in each case, googa-glomping runs faster than competing techniques, and produces glomped widgets that are perfect for squiffling. (6) We expect this new approach to dramatically reduce the cost of squiffled widgets without any loss of quality, and hence make mass production viable.

Some academic journals also have abstracts broken up into separate sections.

See also Using the “executive summary” style: writing that respects your reader’s time [EA · GW] and Reasoning transparency.

(Writing this because it might help me with my actual job one day)

1. I don't belong to the target audience of such posts. But that's why I qualify as a newcomer, whee) and if I tried to make the abstract more academia-styled, I'd get something like this:

John Wentworth’s Natural Abstraction agenda aims to understand and recover “natural” abstractions in realistic environments. We introduce the conceptual framework around it and review its key claims, relationship to prior work in a number of fields, and results to date. Of particular interest are the Natural Abstraction Hypothesis and Wensworth's specific formulation of natural abstractions (here called "redundant information abstractions"). We re-define and draw mathematical proofs for some of the amassed key results. We then discuss the agenda to date including the gaps in theoretical framework and challenge its methodology and relevance to alignment research.

1. What is an agenda? Is it a technical or a common-speech word? (and what are realistic environments, for that matter)
2. "Our hope is to make it easier for newcomers to get up to speed on natural abstractions, as well as to spur a discussion about future research priorities." - unnecessary. It's what people more-or-less usually do anyway.
3. I'd like to change the "specific formulation of natural abstractions" to something more precise, but I don't now the subject.
4. "and explain how those results fit into the agenda" = discuss, but people don't say it because it's just expected of them because math has to be put in context.
5. (just a personal wish) the word "alignment" should preferably be spelled "Alignment" if it's a term or followed by "research".