Seven Key Questions: Clear Focus. Does the abstract make clear what work needed to be done, what problem needed to be solved? Method(s). What method(s) were applied to address the problem? Why these particular methods? Importance. Why should we care about this research? Context. How does this work fit in with other work in the field? Results. What, specifically, are the results? What evidence is given to convince us of those results? Unique Contribution. What does this work report that is new? Possible Applications. In what ways might this work be useful, either theoretically or practically? A single sentence may answer or signal the answer to more than one of the Seven Questions. For example, Importance, Contribution, and Application may well be covered in the same few words, and a clear elucidation of the problem may well include other aspects. What follows is an annotated abstract that illustrates how these Seven Key Questions can be answered even in an abstract with fewer than 100 words. You don’t need to understand the subject to notice how the abstract is put together. Note, too, the use of precise verbs and of transition words that ease the way for any reader. The abstract is from “Directional Hypercomplex Wavelets for Multidimensional Signal Analysis and Processing” by Wai Lam Chan, Hyeokho Choi, and Richard G. Baraniuk, all in the ECE Department at Rice. I have numbered the sentences for easier reference. 1. We extend the wavelet transform to handle multidimensional signals that are smooth save for singularities along lower-dimensional manifolds. 2. We first generalize the complex wavelet transform to higher dimensions using a multidimensional Hilbert transform. 3. Then, using the resulting hypercomplex wavelet transform (HWT) as a building block, we construct new classes of nearly shift-invariant wavelet frames that are oriented along lower-dimensional subspaces. 4. The HWT can be computed efficiently using a 1-D dual-tree complex wavelet transform along each signal axis. 5. We demonstrate how the HWT can be used for fast line detection in 3-D. We extend the wavelet transform to handle multidimensional signals that are smooth save for singularities along lower-dimensional manifolds. Instead of writing the all-too-common passive construction, “The wavelet transform is extended to handle….,” these authors take possession of and responsibility for the work with the opening word, “We.” (Those authors who cannot bring themselves to use “we” even in a multiple-author paper could use “This paper extends” as an alternative.)The verb “extend” not only precisely says what the work does, but also signals context. Clearly, this paper is based on specific prior work on “the wavelet transform” and expands possible applications of the earlier work to specific multidimensional signals. The problem is defined; applications are signaled. As one student said, “There’s a lot riding on that word extend,” and he’s right. Consider what would be lost if the word were the more common (and imprecise) “study” or “discuss.” We first generalize the complex wavelet transform to higher dimensions using a multidimensional Hilbert transform. “First” clearly signals to the reader that there will be more than one step in the method. The rest of the sentence gives details about what was done and links the sentence with the “multidimensional” in the title and in the first sentence. Then, using the resulting hypercomplex wavelet transform (HWT) as a building block, we construct new classes of nearly shift-invariant wavelet frames that are oriented along lower-dimensional subspaces. This second step in the sequence is clearly signaled and then precisely defined. Though the details are left for the body of the paper, enough is given here to illustrate the actual process. The HWT can be computed efficiently using a 1-D dual-tree complex wavelet transform along each signal axis. The shift to passive voice works here because it includes the reader as a possible user of this new technique. “Efficiently” is an important inclusion in any computer-driven research project, in which saving time translates to “saving money.” We demonstrate how the HWT can be used for fast line detection in 3-D. “Demonstrate“ clearly signals results, evidence, and applications, as well as suggesting importance of the work. Repetition of “HWT” reinforces what has newly been added to the field, and “for fast line detection in 3-D” illustrates the promise of the title for a “multidimensional ” application of the wavelet transform. Verb choice in the five sentences illustrates a powerful writing technique. Extend, generalize, construct, computed, and demonstrate are precise and varied. Each verb signals an exact action necessary for the persuasive progression of the argument. My only suggestion would be a substitution of “This paper” or “This work” for one or two of the “we” subjects. In summary, this brief abstract defines the focus of the paper, suggests its context, identifies and applies the methods used, shows why those methods work, gives specific results that echo the promise of the title, indicates what is new, and in the final sentence signals evidence for possible applications of this new technique. Pretty impressive and persuasive in only 95 words!