Title :
Intrinsic features of biomedicai document for the efficient single document summarization
Author_Institution :
Dept. of Comput. Eng., Sungkyunhvan Univ., Suwon, South Korea
Abstract :
In this paper we demonstrate if the similarities between the abstract and individual sections comprised of the document are increased when we apply our devised additional weights based on intrinsic document features for better effective abstraction of a single scientific article belong to biomedical domain. And we treat MLP based experimental results about the prediction of presence or absence of the terms in the abstract, which are with additional weights appeared in the rest of paper except the abstract.
Keywords :
medical computing; statistical analysis; MLP; biomedical domain; intrisic biomedical document features; single document summarization; Abstracts; Computers; Diseases; Educational institutions; Neurons; Predictive models; Software; Additional Weights; DTREG; Intrinsic Features; MLP; Tf idf; WEKA;
Conference_Titel :
Bioinformatics and Biomedicine (BIBM), 2013 IEEE International Conference on
Conference_Location :
Shanghai
DOI :
10.1109/BIBM.2013.6732589