It can be difficult to understand physical measurements (e.g., 28 lb, 600 gallons) that appear in news stories, data reports, and other documents. We develop tools that automatically re-express unfamiliar measurements using the measurements of familiar objects. Our work makes three contributions: (1) we identify effectiveness criteria for objects used in concrete measurement re-expressions; (2) we operationalize these criteria in a scalable method for mining a large dataset of concrete familiar objects with their physical dimensions from Amazon and Wikipedia; and (3) we develop automated concrete re-expression tools that implement three common re-expression strategies (adding familiar context, reunitization and proportional analogy) as energy minimization algorithms. Crowd-sourced evaluations of our tools indicate that people find news articles with re-expressions more helpful and re-expressions help them to better estimate new measurements.
BibTeX
@inproceedings{2018-concrete-reexpression,
title = {Improving Comprehension of Measurements Using Concrete Re-expression Strategies},
author = {Hullman, Jessica AND Kim, Yea-Seul AND Nguyen, Francis AND Speers, Lauren AND Agrawala, Maneesh},
booktitle = {ACM Human Factors in Computing Systems (CHI)},
year = {2018},
url = {https://idl.uw.edu/papers/concrete-reexpression},
doi = {10.1145/3173574.3173608}
}