Automatic Extraction of Key Sentences via Word Sense Identification for Chinese Text Summarization

In this paper, a novel method of key sentences extraction is proposed for automatic Chinese text summarization. Key-senses/sense-patterns discovery and key sentences extraction are its two main components. Since there is no Chinese lexical database like WordNet available to the authors, a compromise is to word-segment, POS-tag a target Chinese text and translate all the nouns/verbs into English for sense disambiguation using WordNet. The characteristic of the proposed method is that each sentence is represented by senses and the key senses in each sentence form a fuzzy transaction. Each entry of the fuzzy transaction is the maximum similarity degree of the corresponding key sense with each of the senses in the sentence. A prototype of this automatic Chinese text summarization scheme is constructed and an intrinsic method with the information-retrieval criteria is used for measuring the summary quality. The results of applying the prototype to datasets with manually-generated summaries are shown.