多文档文摘技术能帮助用户减少不必要的阅读时间,有广阔的应用前景。该文以新闻报道为处理对象,以MMR(Maximal Marginal Relevance)文摘提取算法为基础,针对目前新闻报道往往以专题形式组织展现的特点,提出了一种基于话题的多文档文摘方法。这种方法以话题关键字为打分依据,同时考虑句子位置特征等信息对句子的重要性进行评分。 该文利用TDT4的新闻报道语料对上述文摘方法进行了试验评价,将基于话题的文摘系统和两个Baseline文摘系统进行比较,取得了较好的实验结果,尤其在5%的压缩比例下有明显优势。
Abstract
Multi-document summarization aimed at minimizing unnecessary readings time is of great value nowadays. Considering that news today is usually arranged in topics, this paper takes this advantage and proposes a topic based multi-document summarization method employing MMR. This method treats key words of the topic description as the basis for sentences scoring, together with traditional features such as the sentence position. Experiments results on TDT4 corpus indicate that the proposed method performs better than 2 baseline systems, especially under the compression ratio of 5%.
Key wordsautomatic summarization; topic; natural language process; news
关键词
自动文摘 /
话题 /
自然语言处理 /
新闻
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Key words
automatic summarization /
topic /
natural language process /
news
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参考文献
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脚注
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基金
国家自然科学基金资助项目(61170156); 国家高技术研究发展计划 (863)资助项目( 2010AA012505)
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