A1 Journal article – refereed
Comparison of automatic summarisation methods for clinical free text notes

List of Authors: Hans Moen, Laura-Maria Peltonen, Juho Heimonen, Antti Airola, Tapio Pahikkala, Tapio Salakoski, Sanna Salanterä
Publisher: Elsevier
Publication year: 2016
Journal: Artificial Intelligence in Medicine
Journal acronym: AIIM
Volume number: 67
Issue number: February 2016
eISSN: 1873-2860



A major source of information available in electronic health record (EHR) systems are the clinical free text notes documenting patient care. Managing this information is time-consuming for clinicians. Automatic text summarisation could assist clinicians in obtaining an overview of the free text information in ongoing care episodes, as well as in writing final discharge summaries. We present a study of automated text summarisation of clinical notes. It looks to identify which methods are best suited for this task and whether it is possible to automatically evaluate the quality differences of summaries produced by different methods in an efficient and reliable way.

Methods and materials

The study is based on material consisting of 66,884 care episodes from EHRs of heart patients admitted to a university hospital in Finland between 2005 and 2009. We present novel extractive text summarisation methods for summarising the free text content of care episodes. Most of these methods rely on word space models constructed using distributional semantic modelling. The summarisation effectiveness is evaluated using an experimental automatic evaluation approach incorporating well-known ROUGE measures. We also developed a manual evaluation scheme to perform a meta-evaluation on the ROUGE measures to see if they reflect the opinions of health care professionals.


The agreement between the human evaluators is good (ICC = 0.74, p < 0.001), demonstrating the stability of the proposed manual evaluation method. Furthermore, the correlation between the manual and automated evaluations are high (> 0.90 Spearman's rho). Three of the presented summarisation methods ('Composite', 'Case-Based' and 'Translate') significantly outperform the other methods for all ROUGE measures (p < 0.05, Wilcoxon signed-rank test and Bonferroni correction).


The results indicate the feasibility of the automated summarisation of care episodes. Moreover, the high correlation between manual and automated evaluations suggests that the less labour-intensive automated evaluations can be used as a proxy for human evaluations when developing summarisation methods. This is of significant practical value for summarisation method development, because manual evaluation cannot be afforded for every variation of the summarisation methods. Instead, one can resort to automatic evaluation during the method development process.

Last updated on 2019-29-01 at 16:56