Name
Abstract | ||
|---|---|---|
We propose a novel approach for handling first-time users in the context of automatic report generation from time-series data in the health domain. Handling first-time users is a common problem for Natural Language Generation (NLG) and interactive systems in general - the system cannot adapt to users without prior interaction or user knowledge. In this paper, we propose a novel framework for generating medical reports for first-time users, using multi-objective optimisation (MOO) to account for the preferences of multiple possible user types, where the content preferences of potential users are modelled as objective functions. Our proposed approach outperforms two meaningful baselines in an evaluation with prospective users, yielding large (= .79) and medium (= .46) effect sizes respectively. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/FUZZ-IEEE.2016.7737739 | 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) |
Keywords | Field | DocType |
medical reports,automatic report generation,time-series data,multiobjective optimisation,objective functions,MOO | Natural language generation,Computer science,User knowledge,Natural language,Linear programming,Artificial intelligence,Machine learning,Market research | Conference |
ISSN | ISBN | Citations |
1544-5615 | 978-1-5090-0627-4 | 0 |
PageRank | References | Authors |
0.34 | 26 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dimitra Gkatzia | 1 | 50 | 8.06 |
| Verena Rieser | 2 | 423 | 36.46 |
| Oliver Lemon | 3 | 1072 | 86.38 |