A trainable document summarizer
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Automatic text decomposition using text segments and text themes
Proceedings of the the seventh ACM conference on Hypertext
Automatic text structuring and summarization
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Advantages of query biased summaries in information retrieval
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Summarizing text documents: sentence selection and evaluation metrics
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Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
Finding relevant documents using top ranking sentences: an evaluation of two alternative schemes
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AsianIR '03 Proceedings of the sixth international workshop on Information retrieval with Asian languages - Volume 11
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VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Objectrank: authority-based keyword search in databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
LexRank: graph-based lexical centrality as salience in text summarization
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ECIR'03 Proceedings of the 25th European conference on IR research
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SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Sentence Ordering for Coherent Multi-document Summary Generation
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IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Ontology-Based Natural Query Retrieval Using Conceptual Graphs
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
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Automatic personalized text summarization agent using generic relevance weight based on NMF
ICOIN'09 Proceedings of the 23rd international conference on Information Networking
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Automatic query-based personalized summarization that uses pseudo relevance feedback with NMF
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Comprehension-based result snippets
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Language independent query focused snippet generation
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Sentence length bias in TREC novelty track judgements
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There has been a great amount of work on query-independent summarization of documents. However, due to the success of Web search engines query-specific document summarization (query result snippets) has become an important problem, which has received little attention. We present a method to create query-specific summaries by identifying the most query-relevant fragments and combining them using the semantic associations within the document. In particular, we first add structure to the documents in the preprocessing stage and convert them to document graphs. Then, the best summaries are computed by calculating the top spanning trees on the document graphs. We present and experimentally evaluate efficient algorithms that support computing summaries in interactive time. Furthermore, the quality of our summarization method is compared to current approaches using a user survey.