Some titles stick. "Attention is all you need". "SSA is functional programming". Plus more people see the title than read the abstract. Only one I got to name myself was "Shared Memory Remote Procedure Calls" which I now realise is in exactly the same theme of given the title, you don't need the abstract or the contents.
Different fields use different standard forms or styles for abstracts. If you have not already done so you can get a feel for this by looking at a random sample of the top 100 most-cited papers in your field (or your target conferences, journals, arxiv categories) and quickly compare to top 100 in a couple of disparate fields (e.g. biochemistry, economics, anthropology, mathematics).
Of course, there are good reasons to read a paper besides the quality and content of the abstract. Consequently, there are well known papers with non-standard abstracts. I recommend mastering the basics first.
IMO the most memorable papers contain some unexpected simplification: a complicated problem turns out to reduce to something much simpler [0], perhaps for a counterintuitive reason [1].
So a memorable abstract should advertise that the paper contains some cute little trick. This may not help you actually publish though
[0]: Attack the RLHF problem with a simple classification loss: https://arxiv.org/abs/2305.18290
[1]: Frustratingly Easy Meta-Embeddings: https://arxiv.org/abs/1804.05262
There's also the infamous opening paragraph to the "States of Matter" textbook by David L. Goodstein:
Ludwig Boltzmann, who spent much of his life studying statistical mechanics, died in 1906, by his own hand. Paul Ehrenfest, carrying on the work, died similarly in 1933. Now it is our turn to study statistical mechanics.
Short, succinct and no AI in sight.
- how do you actually go about reading one of these IEEE or arxiv papers?
- do you read only the abstract for most?
- do you read everything in 1 scan and what do you about references? do you follow through all the references?
- do you ask a GPT to help explain? what does your prompt look like?
That said, the depth that I read at mostly depends on why I am reading something. Most papers I don't read in depth, but whether I initially only read the abstract or skim the whole paper depends a lot on a lot of factors (paper content, length, ease of access to full text, whether I am triaging a lot of papers at once, how interested I am in the specific paper (usually based on the title and/or authors, maybe the abstract), etc.).
I read in multiple passes of increasing depth. Zeroth reading is more-or-less triage: is the paper worth reading at all? First reading would generally be start-to-finish. I underline and/or mark up the margins with particularly interesting/important sections and also star key references based on the author's commentary.
If it's an algorithm or math-heavy paper either I only care about the gist and I don't go deeper, or I dig in with further readings and study to properly understand the details. That could be relatively quick, or it could be a whole rabbit hole if I need to learn some background. Sometimes it takes a while for the importance of a paper to dawn on me, in which case I come back later and read in more depth.
With the references I generally only follow those with relevance to my research goals. For example the authors might have detailed a technique in an earlier paper, or they might have cited other relevant work that I was not aware of.
1. First read the abstract and conclusion
2. Then flip through the figures and their captions, and try to understand them as best as you can (though this doesn't work for all papers)
3. Make a deliberate decision about whether it's worth it to dive into the details and read the full text.
LLMs are incredibly useful for parsing dense literature, but I think it's better to ask specific questions about the paper, jargon, equations etc. rather than to have it summarize the paper for you. Summaries are often shallow or even misleading, and they rob you of the opportunity to engage actively with the paper.
include a SUMMARIUM in Latin and ΣΥΝΟΨΙΣ in Greek, for that real Scholar effect.
I don't have any examples to offer; but you may be missing your calling in Marketing
"Abstract The Paxos algorithm, when presented in plain English, is very simple."
For example, I quite enjoyed the paper “Web Browser Fingerprinting Using Only Cascading Style Sheets,” but its abstract isn’t anything overly exciting. The title itself conveyed enough information for me to gather my interest, its attract just confirmed it was probably worth reading it.
So, if you want to be memorable, put in the effort snd have something to say that requires human expertise and the ability to generate non explicit insights.
+1 point for honesty.
-100 for being pathetically pathetic
Smith, Gordon C. S., and Jill P. Pell. 2003. “Parachute Use to Prevent Death and Major Trauma Related to Gravitational Challenge: Systematic Review of Randomised Controlled Trials.” BMJ 327 (7429): 1459–61. https://doi.org/10.1136/bmj.327.7429.1459.
Objectives: To determine whether parachutes are effective in preventing major trauma related to gravitational challenge.
Design: Systematic review of randomised controlled trials.
Data sources: Medline, Web of Science, Embase, and the Cochrane Library databases; appropriate internet sites and citation lists.
Study selection: Studies showing the effects of using a parachute during free fall.
Main outcome measure: Death or major trauma, defined as an injury severity score > 15.
Results: We were unable to identify any randomised controlled trials of parachute intervention.
Conclusions: As with many interventions intended to prevent ill health, the effectiveness of parachutes has not been subjected to rigorous evaluation by using randomised controlled trials. Advocates of evidence based medicine have criticised the adoption of interventions evaluated by using only observational data. We think that everyone might benefit if the most radical protagonists of evidence based medicine organised and participated in a double blind, randomised, placebo controlled, crossover trial of the parachute.