.png){kind=icon}
返回
Sentence selection for generic document summarization using an adaptive differential evolution algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.swevo.2011.06.006.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
For effective multi-document summarization, it is important to reduce redundant information in the summaries and extract sentences, which are common to given documents. This paper presents a document summarization model which extracts key sentences from given documents while reducing redundant information in the summaries. An innovative aspect of our model lies in its ability to remove redundancy while selecting representative sentences. The model is represented as a discrete optimization problem. To solve the discrete optimization problem in this study an adaptive DE algorithm is created. We implemented our model on multi-document summarization task. Experiments have shown that the proposed model is to be preferred over summarization systems. We also showed that the resulting summarization system based on the proposed optimization approach is competitive on the DUC2002 and DUC2004 datasets. (C) 2011 Elsevier Ltd. All rights reserved.
Keyword:
Document summarization
Redundancy
Content coverage
Optimization model
Adaptive differential evolution
期刊
IF:
8.5
论文数:
2.2K
被引数:
1.0W
机构
引用论文
.png){kind=icon}
A differential evolution algorithm with self-adapting strategy and control parameters一种具有自适应策略和控制参数的差分进化算法
PREAI
分享收藏分享收藏Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization求解全局数值优化问题的策略自适应差分进化算法
OAAI
OAAI分享收藏Differential evolution algorithm with ensemble of parameters and mutation strategies具有参数集成和变异策略的差分进化算法
PREAI
分享收藏
查看更多内容