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1. Shang Y, Li Y, Lin H, Yang Z: Enhancing biomedical text summarization using semantic relation extraction. PLoS One; 2011;6(8):e23862
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  • [Source] The source of this record is MEDLINE®, a database of the U.S. National Library of Medicine.
  • [Title] Enhancing biomedical text summarization using semantic relation extraction.
  • 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.
  • [MeSH-major] Abstracting and Indexing as Topic. Algorithms. Information Storage and Retrieval / methods. Natural Language Processing
  • [MeSH-minor] Semantics. Vocabulary, Controlled

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  • (PMID = 21887336.001).
  • [ISSN] 1932-6203
  • [Journal-full-title] PloS one
  • [ISO-abbreviation] PLoS ONE
  • [Language] eng
  • [Publication-type] Journal Article; Research Support, Non-U.S. Gov't
  • [Publication-country] United States
  • [Other-IDs] NLM/ PMC3162578
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