In graph-based extractive text summarization techniques, the weight assigned to the edges of the graph is the crucial parameter for the sentence ranking. The weights associated with the edges are based on the similarity between sentences (nodes). Most of the graph-based techniques use the common words based similarity measure to assign the weight. In this paper, we propose a new graph-based summarization technique, which, besides taking into account the similarity among the individual sentences, also considers the similarity between the sentences and the overall (input) document. While assigning the weight among the edges of the graph, we consider two attributes. The first attribute is the similarity among the nodes, which forms the edges of the graph. The second attribute is the weight given to a component that represents how much the particular edge is similar to the topics of the overall document for which we incorporate the topic modeling. Along with these modifications, we use the semantic measure to find the similarity among the nodes. The evaluation results of the proposed method demonstrate a significant improvement of the summary quality over the existing text summarization techniques.

在基于图的提取文本摘要技术中，分配给图边缘的权重是句子排序的关键参数。与边缘关联的权重基于句子（节点）之间的相似性。大多数基于图的技术都使用基于通用词的相似性度量来分配权重。在本文中，我们提出了一种新的基于图的摘要技术，该技术除了考虑单个句子之间的相似性之外，还考虑了句子与整个（输入）文档之间的相似性。在图的边缘之间分配权重时，我们考虑了两个属性。第一个属性是节点之间的相似性，这形成了图的边缘。第二个属性是赋予组件的权重，它表示特定边缘与我们结合了主题建模的整个文档的主题有多少相似。伴随这些修改，我们使用语义度量来找到节点之间的相似性。与现有的文本摘要技术相比，该方法的评估结果证明摘要质量得到了显着改善。