Exploring Multi Feature Optimization for Summarizing Clinical Trial Descriptions

Documenting Clinical Trial Descriptions of patients can help doctors with diagnostics and treatment plans and can be used for future reference. However, with the rapid growth of population, manually checking all previous files of a patient is not feasible. We address this challenge by providing summaries of clinical trial descriptions. We present a framework for automatically summarizing Clinical Trial Descriptions, which takes advantage of different features in semantic and syntactic space. We propose a multi objective optimization technique which uses position and similarity of the sentences. The similarity is established based on TF-IDF and WMD. We evaluate the proposed method on clinical trial dataset, and compare the results against human gold standard summaries using ROUGE metrics. Our approach is unsupervised in nature which has an advantage over supervised models in the advent of new diseases where a large quantity of quality data is not available. We provide a detailed ablation study to show the contribution of each feature in our approach and release our code on GitHub.
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