Titus likens conference abstract-writing to programming with punch cards:https://plus.google.com/109058993131758006953/posts/Gd8HuxEA1Lh
Recently, I wrote some guidelines for my students on abstract writing:
A CUNY and AMLaP abstract usually allows you to use about 500 words. Here, I provide some guidance on how to write an abstract that is likely to be successful. These guidelines are also the minimal requirements I have when I receive an abstract from you for comments and revisions.
1. Eliminate typos from your abstract. If you use emacs, enable spell check. If you use any LATEX software or Textmate, spelling correction is usually automatically enabled. In my own text editors (TeXShop and Textmate), the word is underlined if there is a typo. In particular, do not mis-spell researchers’ names; they are likely to be your reviewer and will not look at your work kindly if you can’t even spell their name correctly.
2. Learn from your grammar mistakes and never make the same mistake again. There is no credit assigned for being a non-native speaker of English. For better or worse, English is the language of science; this is unlikely to change. You have to find a way to construct grammatical sentences.
3. Make words count. Don’t use more words than you need.
4. The abstract has to have a relatively fixed structure. It is possible to deviate from this pattern presented below, but this requires experience, which takes time. For now, you should stick to this “house style”:
(a) An introductory paragraph which (a) establishes common ground, (b) shakes up the common ground by showing the gap in the topic you will address; and (c) explains the (novel) contribution and its importance to the topic. This section is not the same as the start of a paper; you have very little space, and you have to get to the point right away. You can’t start expansively the way you could in a paper. You should not have extensive references here. The sentences in this section need to have a logical connection that the reviewer will agree with.
A mechanism of predictions implies that the parser has to build up potential upcoming continuations, store them, and then evaluate them given the actual continuation (bottom-up evidence). This raises the question: do individual differences affect the capacity for building-up and maintaining predictions, and for recovering from unpredicted continuations?
No, this mechanism of predictions doesn’t “raise” this question! What the author is really trying to say is that they are going to study prediction at the individual level. The author is taking a novel perspective on the issue, not following up on an implication of previous work.
(b) The description of what you did must have enough detail that the reviewer gets all the relevant information. How to decide what is “enough detail”? Put yourself in the place of the reviewer and ask yourself: if this were an abstract written by someone else, would you be able to figure out what was done? If you have developed a new computational model, you have to motivate its novel aspects. If you have a new experimental design, you have to explain what it achieves. New terms have to be defined.
The similarity difference of matching only a single feature ... to a full match (an item with both features) was estimated ....
Here, the reader has no idea what “similarity difference of matching” means; this was never defined. So, for a reader not willing to think hard about your abstract (which be the case in 100% of reviewers), this sentence means nothing; and yet, it is an important component of the abstract.
(c) Any claims made in the results and discussion sections should be supported by the data. Some common mistakes are (a) to set up an expectation at the start of an abstract that is not met; (b) to make claims that are not substantiated by the data, or worse which are the opposite of what the data in the abstract show. In this section, you should not mention new terms and new ideas that have not been defined before; treating new terms and new ideas as if they are in the common ground is a frequently-occurring mistake.
Numerical difference between the conditions. The trend confirms the results from the literature where the ....
A statistician might buy this claim, but you are writing for psycholinguists, who consider a p-value of less than 0.05 a “true effect”, and anything bigger than 0.05 as “no effect.” You cannot say that you confirmed anything given a null result. Another example:
This suggests that in some cases low cognitive control readers may fail to inhibit the predicted gender form.
In the abstract, there was nothing about “some cases”. The claim is unsupported by data.
(d) The conclusion section should end with a summary statement about the novel finding, and explain the significance of the work. For example, saying things like “More research is needed” does not add any useful information. Another example is to end weakly with something that doesn’t even address the main finding:
Regarding the lack of interactions between condition and either X or Y, we speculate that the sentences were too simple and short in order to capture differential effects.
5. In some abstracts I see fonts changing from one line to the next. Don’t allow that to happen.