Improved extractive summarization of Chinese texts using latent semantic analysis
改进的潜在语义分析中文摘录方法

Chinese extractive summarization is a convenient method to realize Chinese text summarization, which extractes sentences and composites summarization corresponding to the extractive rules. This paper proposed an improved Chinese extractive summarization method using latent semantic analysis by optimizing input matrix and the key sentence selection algorithm. First, the method created multi-valued input matrix based on vector space model. Then it abtained the semantic correlation between sentences and latent conceptionsthe abstract expression of theme by latent semantic analysis for input matrix. At last, it extracted the key sentences by improved optimal selection algorithm. The experimental results show that the respective average for precision, recall and F-measure are 75. 9%, 71. 8% and 73. 8%, and compared with the existing similar methods, the improved method becomes unsupervised completely and makes dramatical improvement of overall, so it has more potential application value.