An Efficient Medical Document Summarization using Sentence Feature Extraction and Ranking

Abstract

Background/Objectives: As all documents related to medical domain do not come with author written summary, the objective is to introduce a summarizer that exploits medical domain-specific knowledge. Methods/Statistical Analysis: Sentence ranking technique has been used to produce high quality summary. The features such as sentence position, length, cue-words (domain-related terms) and acronyms are extracted to assign sentence score. Sentences are ranked and arranged in the decreasing order of their normalized score. The existing summarization approaches in the literature use few or more sentence features but we have opted for few best sentence features. Pre-existing summarizers are used for performance evaluation. Findings: The few best features to be considered in developing medical domain-specific summarizers are sentence position, sentence length, number of cue-words and number of acronyms. Summary produced by any summarizer can be highly informative if and only if it contains dissimilar sentences. Therefore, similarity between sentences is an important feature to be considered for creating highly informative summary. The proposed summarizer is compared with the pre-existing summarizers. The evaluation is done by using traditional metrics such as precision and recall and ROUGE. Not all medical documents come with an author written abstract or summary. So, medical documents with author written abstracts are used to test the performance. Results reveals that the proposed summarizer performs better when compared with existing summarizers and attained ROUGE scores also reveals the same with respect to quality of summary produced. Thus, proposed summarizer provide highly acceptable summary to user. Application/Improvements: Summarization is one of the information retrieval tasks. It helps to determine whether the retrieved document is relevant for in-depth study or not.