An abstract is an advertisement, not an introduction. I’ve spent much of this week working with MSc students writing their dissertations, and this has inevitably led to the part of a dissertation that often causes the most pain to write (and read, for that matter): the abstract. What is the abstract of a report or paper? What it isn’t is an introduction or guide to the rest of the document: that, unsurprisingly, is what the introduction is for. The goal of an abstract is much simpler: it’s intended to persuade the reader to read some or all of the rest of the document. It may be surprising that this is an issue, but in a world in the grip of an information explosion it’s clear that readers’ attention is a limiting quantity in information processing. As a reader, should you bother to read a paper? Or not? And how do you make this decision? Many new researchers, when confronted with a paper, start at the front and work forward. More experienced researchers know this is a mistake that leads to reading whole tracts of irrelevance or nonsense. (The former is worse: nonsense at least has occasional entertainment value.) This leads many people (myself included) to adopt a non-linear reading style:
- Read the abstract
- If still interested, read the conclusion
- If still interested, read the introduction
- If it’s really interesting, read the rest of the paper
- The area of the paper (1 sentence). The problem area to which this paper makes a contribution.
- The issue the paper addresses (1 sentence). Presumably the area is not yet fully explored, and you’ve found a problem that needs tackling — otherwise what’s in your paper?
- What you’ve done, the results you’ve obtained (2 sentences). The key contribution of the paper, what you’ve added to practice and/or knowledge
- What this means (1 sentence). Why should anyone care?
There are lots of interesting animals out there, many of which have horns. No-one has yet reported observing any one-horned horses, however. We describe our research survey of the horses of the West of Ireland. While we found many horses, and many other horned animals, we failed to locate any horned horses. We conclude that further research is required to find unicorns, preferably in an equally pleasant holiday destination.OK, perhaps not a great example. Let’s try another, from a real paper:
In the domain of ubiquitous computing, the ability to identify the occurrence of situations is a core function of being context-aware. Given the uncertain nature of sensor information and inference rules, reasoning techniques that cater for uncertainty hold promise for enhancing the reasoning process. In our work, we apply the Dempster Shafer theory of evidence to infer situation occurrence with the minimal use of training data. We describe a set of evidential operations for sensor mass functions using context quality and evidence accumulation for continuous situation detection. We demonstrate how our approach enables situation inference with uncertain information using a case study based on a published smart home data set.(Taken from McKeever, Ye, Coyle and Dobson. Using Dempster-Shafer theory of evidence for situation inference. In Proceedings of the 4th European Conference on Smart Sensing and Context (EuroSSC). Volume 5741 of LNCS. Springer-Verlag. Guildford, UK. 2009.) The abstract goes from domain to challenge to approach to significance: having read it, the reader hopefully has a fairly good idea of what the paper contributes to which domain, and why this contribution is significant (in the authors’ minds, at least). Shorter is often better, of course:
Wireless sensor networks are attracting increasing interest but suffer from severe challenges such as power constraints and low data reliability. Sensors are often energy-hungry and cannot operate over the long term, and the data they gather are frequently erroneous in complex ways. The two problems are linked, but existing work typically treats them independently: in this paper we consider both side-by-side, and propose a self-organising solution for model-based data collection that reduces errors and communications in a unified fashion.