Automatic Extraction of Key Sentences via Word Sense Identification for Chinese Text Summarization
Yau-Hwang Kuo and Hsun-Hui Huang
CREDIT, Department of Computer Science and Information Engineering, National Cheng Kung University, No.1, Ta-Hsueh Rd., Tainan, Taiwan
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.
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