Saturday, August 29, 2009

How to write paper's abstract

I used to believe writing the abstract is the hardest part in a paper. It has to be complete but concise. It has to define your problem and drive readers interest in that problem. It has to give an idea on the approach you used to solve the problem. You should indicate what kind of results you got, and conclude the work. Too many requirements in too few words. Even though I have all the details in my head, I don't seem to find a way to write this nasty piece of text!

However, after doing a little bit of research on how they should be written, it seems to be one of my favourite sections writing a paper. It all happened when I realized that I don't need to achieve those many requirements in parallel. I can address each of them individually, in series, and end up with a good abstract.

Go over this checklist, provided by Philip Koopman 10 years ago, and write one (you can get away with two, maximum three) sentence on each of them:
- Motivation: Why is your problem interesting? This "sentence" becomes less important if you're doing incremental work on a problem that's widely recognized as important.
- Problem statement: What's the problem you're working on? Which piece of the problem are you trying to solve? Does your work target a special class of the problem?
- Approach: Give a very high level picture on your approach/algorithm.
- Results: If you evaluated your work, it's good to state the results here, in one sentence.
- Conclusion: What are the implications of your work?

Quick notes:
- Unless the conference/journal has specific requirements, an abstract word count of 150 to 200 is common.
- Try to appropriately introduce related keywords to make it more likely for your abstract/paper to appear in search results. However, avoid using too much jargon.


Title: Word-sense disambiguation using statistical methods
Abstract: We describe a statistical technique for assigning senses to words. An instance of a word is assigned a sense by asking a question about the context in which the word appears. The question is constructed to have high mutual information with the translation of that instance in another language. When we incorporated this method of assigning senses into our statistical machine translation system, the error rate of the system decreased by thirteen percent.
Note: Word sense disambiguation is widely recognized as important in the NLP field.

Title: SeRLoc: secure range-independent localization for wireless sensor networks
Abstract: In many applications of wireless sensor networks (WSN), sensors are deployed un-tethered in hostile environments. For location-aware WSN applications, it is essential to ensure that sensors can determine their location, even in the presence of malicious adversaries. In this paper we address the problem of enabling sensors of WSN to determine their location in an un-trusted environment. Since localization schemes based on distance estimation are expensive for the resource constrained sensors, we propose a range-independent localization algorithm called SeRLoc. SeRLoc is distributed algorithm and does not require any communication among sensors. In addition, we show that SeRLoc is robust against severe WSN attacks, such as the wormhole attack, the sybil attack and compromised sensors. To the best of our knowledge, ours is the first work that provides a security-aware range-independent localization scheme for WSN. We present a threat analysis and comparison of the performance of SeRLoc with state-of-the-art range-independent localization schemes.

Title: Abbreviation Expansion Using Information Retrieval
Abstract: Abbreviation is a dynamic and widely used concept in modern languages. However, sometimes abbreviations become a hurdle -for both humans and machines- to understanding part of the text. Many abbreviations have multiple meanings depending on the scope of the text. In this paper we address the problem of selecting the correct expansion of an abbreviation given its context. We propose a novel information retrieval approach to find the expansion most relevant to the contextual text. Our approach achieves an accuracy of 98% compared with 96% achieved by the state-of-the-art approach in the literature, over the publicly available Reuters corpus.