基于词句协同排序的单文档自动摘要算法  

Single document automatic summarization algorithm based on word-sentence co-ranking

作　　者：张璐[1] 曹杰[1] 蒲朝仪 伍之昂[1] 

机构地区：[1]南京财经大学江苏省电子商务重点实验室,南京210023

出　　处：《计算机应用》2017年第7期2100-2105,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(71571093;71372188);国家电子商务信息处理国际联合研究中心项目(2013B01035);江苏省高校自然科学基金资助项目(15KJB520012);南京财经大学校预研究资助项目(YYJ201415)~~

摘　　要：对于节录式自动摘要需要从文档中提取一定数量的重要句子,以生成涵盖原文主旨的短文的问题,提出一种基于词句协同排序的单文档自动摘要算法,将词句关系融入以图排序为基础的句子权重计算过程中。首先给出了算法中词句协同计算的框架;然后转化为简洁的矩阵表示形式,并从理论上证明了收敛性;最后进一步通过去冗余方法提高自动摘要的质量。真实数据集上的实验表明,基于词句协同排序的自动摘要算法较经典的TextRank算法在Rouge指标上提升13%~30%,能够有效提高摘要的生成质量。Focusing on the issue that extractive summarization needs to automatically produce a short summary of a document by concatenating several sentences taken exactly from the original material. A single document automatic summarization algorithm based on word-sentence co-ranking was proposed, named WSRank for short, which integrated the word-sentence relationship into the graph-based sentences ranking model. The framework of co-ranking in WSRank was given, and then was converted to a quite concise form in the view of matrix operations, and its convergence was theoretically proved. Moreover, a redundancy elimination technique was presented as a supplement to WSRank, so that the quality of automatic summarization could be further enhanced. The experimental results on real datasets show that WSRank improves the performance of summarization by 13% to 30% in multiple Rouge metrics, which demonstrates the effectiveness of the proposed method.

关 键 词：自动摘要 节录式摘要 单文档 图排序 词句协同 

分 类 号：TP399[自动化与计算机技术—计算机应用技术] TP391.1[自动化与计算机技术—计算机科学与技术]