A study of Chinese text summarization using adaptive clustering of paragraphs
Automatic summarization is an important research issue in natural language processing. This paper presents a special summarization method to generate single-document summary with maximum topic completeness and minimum redundancy. It initially implements the semantic-class-based vector representations of various kinds of linguistic units in a document by means of HowNet (an existing ontology), which can improve the representation quality of traditional term-based vector space model in a certain degree. Then, by adopting K-means clustering algorithm as well as a clustering analysis algorithm, we can capture the number of different latent topic regions in a document adoptively. Finally, topic representative sentences are selected from each topic region to form the final summary. In order to evaluate the effectiveness of the proposed summarization method, a novel metric which is known as representation entropy is used for summarization redundancy evaluation. Preliminary experimental results show that the proposed method outperforms the conventional basic summarization method under the evaluation scheme when dealing with diverse genres of Chinese documents with free writing style and flexible topic distribution.