基于统计语义和结构特征的自动文摘

Automatic Summarization Based on Statistic Semantic and Structural Features

在分析自动文摘现有方法优缺点的基础上,提出了一种基于统计、语义和结构特征的自动文摘方法。用这些特征构成句子向量表示,并用机器学习的方法对其进行训练得到器,从而把自动文摘转换为分类问题。实验表明,该方法具有较好的重合率。同时,为了解决文摘的冗余和不连贯缺点,进行了一系列的后期处理,提高了文摘的质量。

This paper presents a new automatic summarization method based on statistic, semantic and structural features while the advantages and disadvantages are analyzed for the popular methods of automatic summarization. There are eight features used to form the feature vector for each sentence,and the summarizer is gained by machine learning algorithms ,so automatic summarization is changed into classification task. The experiment results show that the method maintains higher precision. Meanwhile,the paper processes a series of post processing to overcome the shortcoming of redundancy and incoherence, and it improves largely the quality of summary.