中文摘要：

为从大量无关信息中获取有用内容，正文抽取成为Web数据应用不可或缺的组成部分．文中提出一种基于文本密度模型的新闻网页正文抽取方法．主要通过融合网页结构和语言特征的统计模型，将网页文档按文本行转化成正、负密度序列，再根据邻近行的内容连续性，利用高斯平滑技术修正文本密度序列，最后采用改进的最大子序列分割序列抽取正文内容．该方法保持正文完整性并排除噪声干扰，且无需人工干预或反复训练．实验结果表明基于文本密度抽取正文对不同数据源具有广泛的适应性，且准确率和召回率优于现有统计模型．

英文摘要：

In order to obtain useful content encompassed by a large number of irrelevant information, the content extraction becomes indispensable for web data application. An approach of web content extraction based on the text density model is proposed, which integrates page structure features with language features to convert text lines of page document into a positive or negative density sequence. Additionally, the Gaussian smoothing technique is used to revise the density sequence, which takes the content continuity of adjacent lines into consideration. Finally, the improved maximum sequence segmentation is adopted to split the sequence and extract web content. Without any human intervention or repeated trainings, this approach maintains the integrity of content and eliminates noise disturbance. The experimental results indicate that the web content extraction based on the text density model is widely adapted to different data sources, and both accuracy and recall rate of the proposed approach are better than those existing statistical models.