Paper Info
Title
Multi-Document Summarization Based On Two-Level Sparse Representation Model
Abstract
Multi-document summarization is of great value to many real world applications since it can help people get the main ideas within a short time. In this paper, we tackle the problem of extracting summary sentences from multi-document sets by applying sparse coding techniques and present a novel framework to this challenging problem. Based on the data reconstruction and sentence denoising assumption, we present a two-level sparse representation model to depict the process of multi-document summarization. Three requisite properties is proposed to form an ideal reconstructable summary: Coverage, Sparsity and Diversity. We then formalize the task of multi-document summarization as an optimization problem according to the above properties, and use simulated annealing algorithm to solve it. Extensive experiments on summarization benchmark data sets DUC2006 and DUC2007 show that our proposed model is effective and outperforms the state-of-the-art algorithms.
Year
Venue
Keywords
2015
PROCEEDINGS OF THE TWENTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE
sparse coding,summarization
Field
DocType
Citations 
Simulated annealing,Automatic summarization,Data mining,Multi-document summarization,Neural coding,Computer science,Sparse approximation,Submodular set function,Artificial intelligence,Sentence,Optimization problem,Machine learning
Conference
11
PageRank 
References 
Authors
0.55
16
3
Name
Order
Citations
PageRank
He Liu1201.06
Hongliang Yu2296.82
Zhi-Hong Deng318523.33