基于改进TextRank算法的中文文本摘要提取

摘要

This paper proposes a Chinese text summarization extraction algorithm, called DK-TextRank, combines Doc2Vec model, K-means and TextRank algorithm for Chinese texts to improve summarization accuracy.After using the Doc2Vec model for text vectorization, the DK-TextRank algorithm uses an improved K-means algorithm for similar text clustering, and the TextRank algorithm with weight impact factors in each cluster to sort and extract topic sentence.Then, it generates a summary.Experimental results show that, compared with traditional TF-IDF, TextRank algorithm, the DK-TextRank algorithm has an F value of 79.36％ when the number of summary statements is 7, and the extracted abstract has higher quality.%为提高中文文本摘要提取的准确度, 融合Doc2Vec模型、K-means算法和TextRank算法, 提出一种中文文本摘要自动提取算法 (DK-TextRank) .使用Doc2Vec模型进行文本向量化, 采用改进的K-means算法实现相似文本聚类, 在每个聚类簇中应用加入权重影响因子的TextRank算法对文本语句进行排序, 并提取主题句生成摘要.实验结果表明, DK-TextRank算法在摘要语句数量为7时F值达到79.36％, 相比传统TF-IDF、TextRank算法提取的摘要质量更高.