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Measuring Relevance with Named Entity Based Patterns in Topic-Focused Document Summarization

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3 Author(s)
Furu Wei ; Wuhan Univ., Wuhan ; Wenjie Li ; Yanxiang He

In this paper, the role of named entity based patterns is emphasized in measuring the document sentences and topic relevance for topic-focused extractive summarization. Patterns 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 eight pre-specified templates. Question types are also taken into consideration if they are available when dealing with topic questions. To alleviate problems with coverage, pattern 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 tops the best-performing system at Document Understanding Conference (DUC) 2005.

Published in:

Natural Language Processing and Knowledge Engineering, 2007. NLP-KE 2007. International Conference on

Date of Conference:

Aug. 30 2007-Sept. 1 2007