Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27335
Title: Coherent narrative summarization with a cognitive model
Authors: Zhang, R
Li, W 
Liu, N
Gao, D
Keywords: Cognitive modeling
Coherence
Proposition extraction
Summarization
Issue Date: 2016
Publisher: Academic Press
Source: Computer speech and language, 2016, v. 35, p. 134-160 How to cite?
Journal: Computer Speech and Language 
Abstract: For summary readers, coherence is no less important than informativeness and is ultimately measured in human terms. Taking a human cognitive perspective, this paper is aimed to generate coherent summaries of narrative text by developing a cognitive model. To model coherence with a cognitive background, we simulate the long-term human memory by building a semantic network from a large corpus like Wiki and design algorithms to account for the information flow among different compartments of human memory. Proposition is the basic processing unit for the model. After processing a whole narrative in a cyclic way, our model supplies information to be used for extractive summarization on the proposition level. Experimental results on two kinds of narrative text, newswire articles and fairy tales, show the superiority of our proposed model to several representative and popular methods.
URI: http://hdl.handle.net/10397/27335
ISSN: 0885-2308
DOI: 10.1016/j.csl.2015.07.004
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

2
Last Week
0
Last month
0
Citations as of Aug 18, 2017

Page view(s)

53
Last Week
3
Last month
Checked on Aug 20, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.