Extractive summarization using concept‐space and keyword phrase

Abstract

AbstractPROBLEMText summarization is widely used to extract relevant information from documents. The extensive unstructured data are generated in various domains, developing a concise and coherent form of information has become an inevitable and assertive problem among the research community. Automatic text summarization plays a crucial role in acquiring potent knowledge effectively and efficiently for each domain of information. Medical data contains crucial information regarding human diseases and their symptoms. The proposed method will benefit the research community and health experts as it saves a lot of time and resources to compute the patients' summary during diagnosis.APPROACHIn this research, an unsupervised approach is proposed for extractive summarization, based on semantic similarity and keyword‐phrase extraction. Merging Concept Map and the RAKE method, a generic summary is computed based on threshold values. Both single‐document and multi‐document summarization can be accomplished with the approach.RESULTSTo evaluate the proposed unsupervised approach, various biomedical transcripts of neuro‐science, general medicine, gastroenterology, orthopaedic and radiology domain are used. MT Sample Dataset is used to collect 1040 different transcripts. Using the proposed approach, an average ROUGE score of 0.77 for single‐document summarization; however, for generic summary, an average ROUGE is 0.72. The proposed technique is validated for the previous corpus of BioMed articles, and results are better with state‐of‐the‐art techniques.