ANNOTATION GUIDE/ SCHEME FOR PAPER ABSTRACT
In this Annotation guide/ scheme for paper abstract, here are the main parts of the abstract we’d like you to highlight:
- Background/implications: What motivates the problem/question being addressed in this paper? What is the current state of the world for the problem/question? Why is this problem/question important (for society/other scientists)?
- Purpose: What is the specific/immediate thing the paper’s authors want to create or know?
- Mechanism: If purpose is to create a ‘system/method/model’: What is the proposed system/method/model, and how does it work?
- Research Method(s): If purpose is to produce ’empirical’ findings: What research methods were used to answer the research question? If purpose is ‘system/method/model’: How did the authors evaluate their solution?
- Findings: If purpose is to produce ’empirical’ findings: What did the authors find out? If purpose is ‘system/method/model’: Did the solution work?
Annotation Guide/ Scheme for Paper Abstract Examples
Social Eye Tracking: Gaze Recall with Online Crowds – See Here
Eye tracking is a compelling tool for revealing people’s spatial-temporal distribution of visual attention.
But Quality eye tracking hardware is expensive and can only be used with one person at a time.
Further, webcam eye tracking systems have significant limitations on head movement and lighting conditions that result in significant data loss and inaccuracies.
To address these drawbacks, we introduce a new approach that harnesses the crowd to understand allocation of visual attention.
In our approach, crowd-sourcing participants use mouse clicks to self report the positions and trajectory for the following valuable eye tracking measures: first gaze, last gaze and all gazes.
We validate our crowd-sourcing approach with a user study, which demonstrated good accuracy when compared to a real eye tracker.
We then deployed our prototype, GazeCrowd, in a crowd-sourcing setting, and showed that it accurately generated gaze heatmaps and trajectory maps.
Such an approach will allow designers to evaluate and refine their visual design without requiring the use of limited/ expensive eye trackers.
What Makes a Strong Team? Using Collective Intelligence to Predict Team Performance in League of Legends.
Recent research has demonstrated that (a) groups can be characterised by a collective intelligence (CI) factor that measures their ability to perform together on a wide range of different tasks, and (b) this factor can predict group’ performance on other tasks in the future.
The current study examines whether these results translate into the world of teams in competitive online video games where self-organised, time-pressured, and intense collaboration occurs purely online.
In this study of teams playing the online game League of Legends, we find that CI does, indeed, predict the competitive performance of teams controlling for the amount of time played as a team.
We also find that CI is positively correlated with the presence of a female team member and with the team members’ average social perceptiveness.
Finally, unlike in prior studies, tacit coordination in this setting plays a larger role than verbal communication.
Style guidelines for Paper Abstract
We eventually hope to train a machine learning model to help produce a first draft of annotations for as many papers as we can. Therefore, it’s important that we try to be as consistent as possible with the cue/signal words/phrases that we are using to identify the annotation aspects. As a general rule, we prefer if the cue/signal words/phrases are consistently placed in the *beginning* or *end* of a segment.
Here are some example cues/signals. Please feel free to add your own!
For purpose annotation segments:
- in order to
- to produce
- to enable
- in this paper, we examine
- in this paper, we investigate
- we present
- we give
- in this paper, a method is presented that
- hypothesis (understand papers)
- how to
- X is challenging
- in this paper, we propose a new, simple mechanism to…
- to aid in
- in this paper, we report
- in this paper, we explore
For mechanism segments:
- we propose <specific, unique words>
- that (end)
- we develop
- to address <issue>, we…
- we introduce
- we applied
For findings segments:
- allows (late, for systems papers)
- showed that
- is able to
- we show that
- indicative mood
- we find that
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