Advantages of query biased summaries in information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Bringing order to the Web: automatically categorizing search results
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Generating hierarchical summaries for web searches
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Proceedings of the 13th international conference on World Wide Web
Learning to cluster web search results
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic evaluation of summaries using N-gram co-occurrence statistics
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Clustering versus faceted categories for information exploration
Communications of the ACM - Supporting exploratory search
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Topic sentiment mixture: modeling facets and opinions in weblogs
Proceedings of the 16th international conference on World Wide Web
Tag clouds for summarizing web search results
Proceedings of the 16th international conference on World Wide Web
Learn from web search logs to organize search results
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Learning query-biased web page summarization
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Mining multi-faceted overviews of arbitrary topics in a text collection
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploratory Search
Mining broad latent query aspects from search sessions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-document summarization by sentence extraction
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLP Workshop on Automatic Summarization
Multi-document summarization by maximizing informative content-words
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multi-document summarization using sentence-based topic models
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
The Probabilistic Relevance Framework: BM25 and Beyond
Foundations and Trends in Information Retrieval
Topic aspect analysis for multi-document summarization
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Multi-dimensional search result diversification
Proceedings of the fourth ACM international conference on Web search and data mining
Identifying aspects for web-search queries
Journal of Artificial Intelligence Research
Inferring query aspects from reformulations using clustering
Proceedings of the 20th ACM international conference on Information and knowledge management
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Conventional search engines usually return a ranked list of web pages in response to a query. Users have to visit several pages to locate the relevant parts. A promising future search scenario should involve: (1) understanding user intents; (2) providing relevant information directly to satisfy searchers' needs, as opposed to relevant pages. In this paper, we present a search paradigm to summarize a query's information from different aspects. Query aspects could be aligned to user intents. The generated summaries for query aspects are expected to be both specific and informative, so that users can easily and quickly find relevant information. Specifically, we use a Composite Query for Summarization" method, where a set of component queries are used for providing additional information for the original query. The system leverages the search engine to proactively gather information by submitting multiple component queries according to the original query and its aspects. In this way, we could get more relevant information for each query aspect and roughly classify information. By comparative mining the search results of different component queries, it is able to identify query (dependent) aspect words, which help to generate more specific and informative summaries. The experimental results on two data sets, Wikipedia and TREC ClueWeb2009, are encouraging. Our method outperforms two baseline methods on generating informative summaries.