A new re-ranking method for generic chinese text summarization and its evaluation

Authors:
Xiaojun Wan;Yuxin Peng
Affiliations:
Institute of Computer Science and Technology, Peking University, Beijing, China;Institute of Computer Science and Technology, Peking University, Beijing, China
Venue:
ICADL'05 Proceedings of the 8th international conference on Asian Digital Libraries: implementing strategies and sharing experiences
Year:
2005

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Abstract

In this paper a new EMD-MMR (EMD: earth mover's distance; MMR: maximal marginal relevance) re-ranking method is proposed for generic Chinese text summarization. Our extraction-based summarization approach first ranks the sentences in a document by their weight calculated based on word frequency and position, and then re-ranks a few highly weighted sentences by the EMD-MMR method for sentence extraction. The proposed re-ranking method adopts a novel EMD-based similarity metric instead of the Cosine metric into the MMR approach. The EMD-based similarity metric can naturally take into account the semantic relatedness between words and compute the semantic similarity between texts with a many-to-many matching among words. We evaluate the performance of the proposed approach with a novel nk-blind method and the results demonstrate its effectiveness.