基于改进的MMR算法的新闻文本抽取式摘要方法

提出了基于最大边缘相关（maximal marginal relevance，MMR）的新闻摘要方法以及基于支持向量机（support vector machine，SVM）和MMR相结合的新闻摘要方法。其中，第1种方法是对传统MMR模型进行了改进，第2种方法使用了改进MMR模型对SVM分类结果进行了二次选择。实验表明：相比于传统MMR模型，该文提出的基于改进MMR的摘要方法和基于SVM-MMR的摘要方法的平均准确率分别提升了0.148、0.204，且基于MMR的新闻摘要方法的摘要效率约为基于SVM-MMR的摘要方法的3倍。改进的MMR算法更加适用于对摘要效率要求高的应用场景，特别是对长文本进行摘要。基于SVM-MMR的摘要方法则更适用于生成对文本内容覆盖相对全面的摘要。

News Summarization Extracting Method Based on Improved MMR Algorithm

This paper proposes a news extraction method based on maximal marginal relevance (MMR) and a news extraction method based on support vector machine and maximal marginal relevance (SVM-MMR). The first method improves the traditional MMR news extraction method, and the second one uses the improved MMR news extraction method to make a second choice of the SVM classification results. Compared with the traditional MMR news extraction method, the average precision of MMR-based and SVMMMR-based news extraction methods are improved by 0.148 and 0.204, respectively. And the extraction efficiency of the MMR-based method is about 3 times of that of the SVMMMR method. The augmented MMR algorithm is more suitable for application scenarios that require high summarization efficiency, especially for long text summarization, while the SVM-MMR method is more suitable for generating a more comprehensive summary of the text content.