I'm a bit afraid that some people will read this article or skim it and say "The fact that you have to do all of this 'branding' is just further proof that science is riddled with irredeemable incentive issues." However, this isn't the author's point. In fact, early the the post, the author writes:
>The tweaks that get the paper accepted—unexpectedly, happily—also improve the actual science contribution.
>The main point is that your paper’s value should be obvious, not that is must be enormous.
This is slightly oversimplified, but from the outside, science may look like researchers are constantly publishing papers sort of for the sake of it. However, the papers are the codified ways in which we attempt to influence the thinking of other researchers. All of us who engage in scientific research aim to be on the literal cutting edge of the research conversation. Therefore it's imperative to communicate how our work can be valuable to specific readers.
Let's take a look at the two abstracts:
(Version 1, Rejected): Given two distinct stimuli, humans can compare and contrast them using natural language. The comparative language that arises is grounded in structural commonalities of the subjects. We study the task of generating comparative language in a visual setting, where two images provide the context for the description. This setting offers a new approach for aiding humans in fine grained recognition, where a model explains the semantics of a visual space by describing the difference between two stimuli. We collect a dataset of paragraphs comparing pairs of bird photographs, proposing a sampling algorithm that leverages both taxonomic and visual metrics of similarity. We present a novel model architecture for generating comparative language given two images as input, and validate its performance both on automatic metrics and visa human comprehension.
Here, the first two sentences a) make a really obvious claim and could equally be at home in a philosophy journal, a linguistic journal, a cognitive science journal, a psychology journal, a neuroscience journal, even something about optometry. Moreover, some readers may look at this abstract and think "well, that's nice, but I'm not sure I need to read this."
(Version 2, Accepted): We introduce the new Birds-to-Words dataset of 41k sentences describing fine-grained differences between photographs of birds. The language collected is highly detailed, while remaining understandable to the everyday observer (e.g., “heart-shaped face,” “squat body”). Paragraph-length descriptions naturally adapt to varying levels of taxonomic and visual distance—drawn from a novel stratified sampling approach—with the appropriate level of detail. We propose a new model called Neural Naturalist that uses a joint image encoding and comparative module to generate comparative language, and evaluate the results with humans who must use the descriptions to distinguish real images. Our results indicate promising potential for neural models to explain differences in visual embedding space using natural language, as well as a concrete path for machine learning to aid citizen scientists in their effort to preserve biodiversity.
Compared to V1, the V2 abstract does a much better job of communicating a) how this project might be valuable to people who want to understand and use neural-network models "to explain differences in visual embedding space using natural language." Or to put it another way, if you want to understand this, it's in your interest to read the paper!