A full-text retrieval system with a dynamic abstract generation function
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic text structuring and summarization
Information Processing and Management: an International Journal - Special issue: methods and tools for the automatic construction of hypertext
Advantages of query biased summaries in information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
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
Summarizing text documents: sentence selection and evaluation metrics
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
New Methods in Automatic Extracting
Journal of the ACM (JACM)
A task-oriented study on the influencing effects of query-biased summarisation in web searching
Information Processing and Management: an International Journal
A system for query-specific document summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
The influence of caption features on clickthrough patterns in web search
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
Query biased snippet generation in XML search
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Enhancing diversity, coverage and balance for summarization through structure learning
Proceedings of the 18th international conference on World wide web
Including summaries in system evaluation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Interactive retrieval based on faceted feedback
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Constructing query-biased summaries: a comparison of human and system generated snippets
Proceedings of the third symposium on Information interaction in context
Multi-document summarization by graph search and matching
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Information Processing and Management: an International Journal
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As highly structured documents with rich metadata (such as products, movies, etc.) become increasingly prevalent, searching those documents has become an important IR problem. Unfortunately existing work on document summarization, especially in the context of search, has been mainly focused on unstructured documents, and little attention has been paid to highly structured documents. Due to the different characteristics of structured and unstructured documents, the ideal approaches for document summarization might be different. In this paper, we study the problem of summarizing highly structured documents in a search context. We propose a new summarization approach based on query-specific facet selection. Our approach aims to discover the important facets hidden behind a query using a machine learning approach, and summarizes retrieved documents based on those important facets. In addition, we propose to evaluate summarization approaches based on a utility function that measures how well the summaries assist users in interacting with the search results. Furthermore, we develop a game on Mechanical Turk to evaluate different summarization approaches. The experimental results show that the new summarization approach significantly outperforms two existing ones.