Automatic multi-document summarization is an outgrowth of single document summarization.
文本自动综述是自动文摘在多文档上的推广。
The multi-document summarization technology is becoming a research focus in the field of natural language processing.
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。
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。
The research can be summarized as follows:Firstly, for query-focus multi-document summarization, this paper proposed the strategy of summary sentence selection using keywords extraction.
本文的主要工作包括以下几个方面：第一，本文提出了一种以关键词语抽取为核心的文摘句选择策略。
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.
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.
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。
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