Feature based cluster ranking approach for single document summarization

Author(s):  
Aakanksha Sharaff ◽  
Mohit Jain ◽  
Geethika Modugula
2013 ◽  
Author(s):  
Yoo-Kang Ji ◽  
◽  
Yong-Il Kim ◽  
Sun Park ◽  
◽  
...  

Author(s):  
Jiwei Li ◽  
Sujian Li

Supervised learning methods and LDA based topic model have been successfully applied in the field of multi-document summarization. In this paper, we propose a novel supervised approach that can incorporate rich sentence features into Bayesian topic models in a principled way, thus taking advantages of both topic model and feature based supervised learning methods. Experimental results on DUC2007, TAC2008 and TAC2009 demonstrate the effectiveness of our approach.


2015 ◽  
Author(s):  
Paul Dimitri ◽  
Karim Lekadir ◽  
Corne Hoogendoorn ◽  
Paul Armitage ◽  
Elspeth Whitby ◽  
...  

Informatica ◽  
2010 ◽  
Vol 21 (3) ◽  
pp. 361-374 ◽  
Author(s):  
Antanas Lipeika

Informatica ◽  
2017 ◽  
Vol 28 (3) ◽  
pp. 439-452
Author(s):  
Mykolas J. Bilinskas ◽  
Gintautas Dzemyda ◽  
Mantas Trakymas
Keyword(s):  
Ct Scan ◽  

2016 ◽  
Vol 136 (8) ◽  
pp. 1078-1084
Author(s):  
Shoichi Takei ◽  
Shuichi Akizuki ◽  
Manabu Hashimoto

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