Abstract: 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.

Key words: automatic summarization, extractive summary, single document, graph-based ranking, word-sentence collaboration

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。

关键词: 自动摘要, 节录式摘要, 单文档, 图排序, 词句协同