计算机应用研究2017，Vol.34Issue(10)：2892-2896,2928,6.DOI:10.3969/j.issn.1001-3695.2017.10.003
基于矩阵分解和子模最大化的微博新闻摘要方法
Weibo-oriented news summarization based on matrix factorization and submodular maximization
摘要
Abstract
This paper presented a novel method for Weibo-oriented Chinese new summarization which combined matrix factorization and submodular maximization.It used the orthogonal matrix factorization(OrMF) model to solve the information sparsity issue of short texts and the information redundancy problem in the projection procedure,and obtained robust latent vectors for news sentences.Moreover,it evaluated news sentences for its relevance and diversity.The objective function included several submodular functions and a non-submodular function that evaluated sentence dissimilarities.Finally,it designed a greedy algorithm to select summary sentences.Experimental results on NLPCC2015 datasets show that the ROUGE scores of the proposed method outweigh other baseline systems and that the quality of Weibo-oriented news summaries is improved effectively.关键词
子模属性/正交矩阵分解/新闻摘要/抽取式摘要/微博Key words
submodularity/orthogonal matrix factorization/news summarization/extractive summarization/Weibo分类
信息技术与安全科学引用本文复制引用
刘彼洋,孙锐,姬东鸿..基于矩阵分解和子模最大化的微博新闻摘要方法[J].计算机应用研究,2017,34(10):2892-2896,2928,6.基金项目
国家社科重大招标计划资助项目(11&ZD189) （11&ZD189）
国家自然科学基金面上资助项目 (61373108) （61373108）
