Exploring Domain-Sensitive Features for Extractive Summarization in the Medical Domain

This paper describes experiments to adapt document summarization to the medical domain. Our summarizer combines linguistic features corresponding to text fragments (typically sentences) and applies a machine learning approach to extract the most important text fragments from a document to form a summary. The generic features comprise features used in previous research on summarization. We propose to adapt the summarizer to the medical domain by adding domain-specific features. We explore two types of additional features: medical domain features and semantic features. The evaluation of the summarizer is based on medical articles and targets different aspects: i) the classification of text fragments into ones which are important and ones which are unimportant for a summary; ii) analyzing the effect of each feature on the performance; and iii) system improvement over our baseline summarizer when adding features for domain adaptation. Evaluation metrics include accuracy for training the sentence extraction and the ROUGE measure computed for reference summaries. We achieve an accuracy of 84.16% on medical balanced training data by using an IB1 classifier. Training on unbalanced data achieves higher accuracy than training on balanced data. Domain adaptation using all domain-specific features outperforms the baseline summarization wrt. ROUGE scores, which shows the successful domain adaptation with simple means.