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.
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||10th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2008|
|Period||15/12/08 → 19/12/08|
- Named entity based association
- Query-oriented summarization
- Computer Science(all)
- Theoretical Computer Science