Exploiting the Role of Named Entities in Query-Oriented Document Summarization

Authors:
Wenjie Li;Furu Wei;Ouyang You;Qin Lu;Yanxiang He
Affiliations:
Department of Computing, The Hong Kong Polytechnic University, Hong Kong;Department of Computing, The Hong Kong Polytechnic University, Hong Kong and Department of Computer Science and Technology, Wuhan University, China;Department of Computing, The Hong Kong Polytechnic University, Hong Kong;Department of Computing, The Hong Kong Polytechnic University, Hong Kong;Department of Computer Science and Technology, Wuhan University, China
Venue:
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Year:
2008

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Abstract

In this paper, we exploit the role of named entities in measuring document/query sentence relevance in query-oriented extractive summarization. Named entity driven associations are defined as the informative, semantic-sensitive text bi-grams consisting of at least one named entity or the semantic class of a named entity. They are extracted automatically according to seven pre-defined templates. Question types are also taken into consideration if they are available when dealing with query questions. To alleviate problems with low coverage, named entity based association and uni-gram models are integrated together to compensate each other in similarity calculation. Automatic ROUGE evaluations indicate that the proposed idea can produce a very good system that among the best-performing system at the DUC 2005.