Utilization of Summarization Algorithms for a Better Understanding of Clustered Medical Documents
Ravi Seeta Sireesha1, P. S. Avadhani2
1Mrs. Ravi Seeta Sireesha, Department of Computer Science and Systems Engineering, Andhra University, Visakhapatnam, India.
2Prof. P S Avadhani, Professor, Department of Computer Science and Systems Engineering, Andhra University, Visakhapatnam, India.
Manuscript received on November 19, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 3077-3083 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B4409129219/2019©BEIESP | DOI: 10.35940/ijeat.B4409.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Medical documents contain rich information about the diseases, medication, symptoms and precautions. Extraction of useful information from large volumes of medical documents that are generated by electronic health record systems is a complex task as they are unstructured or semi-structured. Various partitional and agglomerative clustering techniques are applied for grouping the medical documents into meaningful clusters . Multi-document summarization techniques which are recent development in the field of Natural Language Processing are applied to condense the huge data present in the clustered medical documents to generate a single summary which conveys the key meaning. The summarization techniques can be broadly classified into two types . They are: Extractive Summarization techniques and Abstractive Summarization techniques. Extractive Summarization techniques try to retrieve the most important sentences from the given document. Abstractive Summarization techniques try to generate summary with new sentences which are not present in the document. Extractive summarization techniques using Statistical Approaches are applied on the clustered medical documents. Medical summaries help the patients for a better and prior understanding of the disease and they can get a brief idea before consulting a physician. The generated summaries are evaluated using ROUGE (Recall Oriented Understudy of Gisting Evaluation) evaluation technique.
Keywords: Partitional and Agglomerative Clustering techniques, Multi-document summarization techniques.