一种基于云模型的文摘单元选取方法研究

A Summarization Unit Selecting Method Based on Cloud Model

该文提出了一种基于云模型的文摘单元选取方法,利用云模型,全面考虑文摘单元的随机性和模糊性,提高面向查询的多文档自动文摘系统的性能。首先计算文摘单元和查询条件的相关性,将文摘单元和各个查询词的相关度看成云滴,通过对云的不确定性的计算,找出与查询条件真正意义相关的文摘单元;随后利用文档集合重要度对查询相关的结果进行修正,将文摘句和其他各文摘句的相似度看成云滴,利用云的数字特征计算句子重要度,找出能够概括尽可能多的文档集合内容的句子,避免片面地只从某一个方面回答查询问题。为了证明文摘单元选取方法的有效性,在英文大规模公开语料上进行了实验,并参加了国际自动文摘公开评测,取得了较好的成绩。

This paper proposes a summarization unit selection method based on the cloud model. The cloud model is used to consider randomness as well as fuzziness on distribution of summarization unit. In obtaining relevance be- tween summarization unit and query, the scores of relevance between the word and each query word are seen as cloud drops. According to the uncertainty of cloud, a summarization unit which is more relevant to the query is given higher score. After that, the importance in the document set is also considered to evaluate the sentence＇s ability to summarize content of the document set. Similarities between a sentence and all sentences in document set are considered as cloud drops. All these cloud drops become a cloud, which indicates the sentence＇s ability to summarize content of the document set. The effectiveness of the proposed method is demonstrated on large-scale open benchmark corpus in English. The method was also examined by TAC （Text Analysis conference） 2010 with satisfactory results.