The use of domain-specific concepts in biomedical text summarization

  • Authors:

  • Lawrence H. Reeve;Hyoil Han;Ari D. Brooks

  • Affiliations:

  • Drexel University, College of Information Science and Technology, Philadelphia, PA, USA;Drexel University, College of Information Science and Technology, Philadelphia, PA, USA;Drexel University, College of Medicine, Philadelphia, PA, USA

  • Venue:
  • Information Processing and Management: an International Journal
  • Year:
  • 2007

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Abstract

Text summarization is a method for data reduction. The use of text summarization enables users to reduce the amount of text that must be read while still assimilating the core information. The data reduction offered by text summarization is particularly useful in the biomedical domain, where physicians must continuously find clinical trial study information to incorporate into their patient treatment efforts. Such efforts are often hampered by the high-volume of publications. This paper presents two independent methods (BioChain and FreqDist) for identifying salient sentences in biomedical texts using concepts derived from domain-specific resources. Our semantic-based method (BioChain) is effective at identifying thematic sentences, while our frequency-distribution method (FreqDist) removes information redundancy. The two methods are then combined to form a hybrid method (ChainFreq). An evaluation of each method is performed using the ROUGE system to compare system-generated summaries against a set of manually-generated summaries. The BioChain and FreqDist methods outperform some common summarization systems, while the ChainFreq method improves upon the base approaches. Our work shows that the best performance is achieved when the two methods are combined. The paper also presents a brief physician's evaluation of three randomly-selected papers from an evaluation corpus to show that the author's abstract does not always reflect the entire contents of the full-text.