![]() Computational Linguistics 16(1), 22–29 (1990)Ĭonroy, J.M., Schlesinger, J.D., O’Leary, D.P., Goldstein, J.: Back to basics: Classy 2006. Edinburgh University Press, UK (1998)Ĭhurch, K.W., Hanks, P.: Word association norms, mutual information, and lexicography. ![]() Artificial Intelligence in Medicine 33(2), 157–177 (2005)Īston, G., Burnard, L.: The BNC Handbook: Exploring the British National Corpus with SARA. This process is experimental and the keywords may be updated as the learning algorithm improves.Īfantenos, S., Karkaletsis, V., Stamatopoulos, P.: Summarization from medical documents: A survey. These keywords were added by machine and not by the authors. The experiments, both on a generic single document summarization evaluation, and on a query-based multi-document evaluation, verify the effectiveness of the proposed measures and show that the proposed approach achieves a state-of-the-art performance. In a query-based summarization setting, the correlation between user queries and sentences to be scored is established from both the micro (i.e. at the word level) and the macro (i.e. at the sentence level) perspectives, resulting in an effective ranking formula. In this paper, we propose a new quantification measure for word significance used in natural language processing (NLP) tasks, and successfully apply it to an extractive text summarization approach. Document summarization can be viewed as a reductive distilling of source text through content condensation, while words with high quantities of information are believed to carry more content and thereby importance.
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