The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Applying summarization techniques for term selection in relevance feedback
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Generic summaries for indexing in information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Summarization as feature selection for text categorization
Proceedings of the tenth international conference on Information and knowledge management
On Effective Conceptual Indexing and Similarity Search in Text Data
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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Document similarity search is to find documents similar to a query document in a text corpus and return a ranked list of documents to users, which is widely used in recommender systems in library or web applications. The popular approach to similarity search is to calculate the similarities between the query document and documents in the corpus and then rank the documents. In this paper, we investigate the use of document summarization techniques to improve the effectiveness of document similarity search. In the proposed summary-based approach, the query document is summarized and similarity searches are performed with the new query of the produced summary instead of the original document. Different retrieval models and different summarization methods are investigated in the experiments. Experimental results demonstrate the higher effectiveness of the summary-based similarity search.