该方法利用文档内和文档之间段落的语义相关性，实现了多文档文摘的自动生成。
By using semantic correlation among paragraphs in an article and that among articles, automatic generation of multi-document summary was implemented.
多文档自动文摘技术日益成为自然语言处理领域的一个研究热点。
The multi-document summarization technology is becoming a research focus in the field of natural language processing.
针对面向查询的多文档自动文摘，本文提出了一种系统实现方法。
In this paper, we propose an approach to achieve a system for query-focused multi-document summarization.
文本自动综述是自动文摘在多文档上的推广。
Automatic multi-document summarization is an outgrowth of single document summarization.
句子间相似度的计算在自然语言处理的各个领域都占有很重要的地位，在多文档自动文摘技术中，句子间相似度的计算是一个关键的问题。
Sentence similarity computation is very important in all the fields of Natural Language Processing. In Multi-document Summarization Technology, sentence similarity computation is a key problem.
这种方法能够保证文摘对多文档集合的各个重要子主题有较好的反映，而文摘本身冗余度较低。
Summary generated by this method can reflect all the important subtopics well, and also has a lower redundancy.
为解决词频矩阵的词频维数过大和矩阵过于稀疏的问题，提出一种子主题区域划分的多文档自动文摘方法。
In order to solve the greatly dimension of word frequency and sparse matrix, this paper proposes a multi-document summarization method based on sub-topics area partition.
为解决词频矩阵的词频维数过大和矩阵过于稀疏的问题，提出一种子主题区域划分的多文档自动文摘方法。
In order to solve the greatly dimension of word frequency and sparse matrix, this paper proposes a multi-document summarization method based on sub-topics area partition.
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