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

摘要

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 prespecified 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.