Abstract
Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g., H1N1 disease, from multiple documents based on semantic relation extraction. Our approach includes three stages: 1) We extract semantic relations in each sentence using the semantic knowledge representation tool SemRep. 2) We develop a relation-level retrieval method to select the relations most relevant to each query concept and visualize them in a graphic representation. 3) For relations in the relevant set, we extract informative sentences that can interpret them from the document collection to generate text summary using an information retrieval based method. Our major focus in this work is to investigate the contribution of semantic relation extraction to the task of biomedical text summarization. The experimental results on summarization for a set of diseases show that the introduction of semantic knowledge improves the performance and our results are better than the MEAD system, a well-known tool for text summarization.
Highlights
The volume of biomedical literature is growing rapidly in recent years
Experimental Design It is well known that the evaluation of text summarization is extremely difficult in IR domain even for human being, since different users may be interested in different aspects of the query in different applications, leaving much flexibility to determine the accuracy of a text summary
Existing evaluation approaches for text summarization relies on comparing the text summary generated by computer with a ‘‘gold standard’’ given by human experts [12]
Summary
The volume of biomedical literature is growing rapidly in recent years. The number of articles indexed in PubMed is over 19 million. The huge text collection brings a big challenge for human experts to find the information they need. The technique of automatic text summarization can grasp the general information and key points of a certain topic and make the process of knowledge discovery efficient. Biologists often need to find the general information about a biological concept, e.g., a gene or a disease, from multiple documents without reading all sentences within the full-text. In this case, an accurate text summarization system can be greatly helpful
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