Name
Title | ||
|---|---|---|
Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records. |
Abstract | ||
|---|---|---|
Extractive summarization is very useful for physicians to better manage and digest Electronic Health Records (EHRs). However, the training of a supervised model requires disease-specific medical background and is thus very expensive. We studied how to utilize the intrinsic correlation between multiple EHRs to generate pseudo-labels and train a supervised model with no external annotation. Experiments on real-patient data validate that our model is effective in summarizing crucial disease-specific information for patients. |
Year | Venue | DocType |
|---|---|---|
2018 | arXiv: Computation and Language | Journal |
Volume | Citations | PageRank |
abs/1811.08040 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangan Liu | 1 | 0 | 0.34 |
| Keyang Xu | 2 | 9 | 2.38 |
| Pengtao Xie | 3 | 339 | 22.63 |
| Bo Xing | 4 | 7332 | 471.43 |