A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Variations in relevance judgments and the measurement of retrieval effectiveness
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
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Generic text summarization using relevance measure and latent semantic analysis
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A new approach to unsupervised text summarization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Text summarization via hidden Markov models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Novelty and redundancy detection in adaptive filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
A mathematical theory of communication
ACM SIGMOBILE Mobile Computing and Communications Review
Learning to Probabilistically Identify Authoritative Documents
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Automatic Text Summarization Using a Machine Learning Approach
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Beyond independent relevance: methods and evaluation metrics for subtopic retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
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
Query-relevant summarization using FAQs
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Query-sensitive similarity measures for information retrieval
Knowledge and Information Systems
Less is more: probabilistic models for retrieving fewer relevant documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Query-Sensitive Similarity Measure for Content-Based Image Retrieval
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Novelty and topicality in interactive information retrieval
Journal of the American Society for Information Science and Technology
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Multi-document summarization using cluster-based link analysis
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Novelty and diversity in information retrieval evaluation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Enhancing diversity, coverage and balance for summarization through structure learning
Proceedings of the 18th international conference on World wide web
Measuring importance and query relevance in topic-focused multi-document summarization
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Multi-document summarization by sentence extraction
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLP Workshop on Automatic Summarization
Document summarization using conditional random fields
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Manifold-ranking based topic-focused multi-document summarization
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Link analysis, eigenvectors and stability
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Joint sentiment/topic model for sentiment analysis
Proceedings of the 18th ACM conference on Information and knowledge management
Graph-based multi-modality learning for topic-focused multi-document summarization
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
The automatic creation of literature abstracts
IBM Journal of Research and Development
Multi-document summarization using sentence-based topic models
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Exploiting novelty, coverage and balance for topic-focused multi-document summarization
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Multi-document summarization via the minimum dominating set
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Hi-index | 0.00 |
Summarization plays an increasingly important role with the exponential document growth on the Web. Specifically, for query-focused summarization, there exist three challenges: (1) how to retrieve query relevant sentences; (2) how to concisely cover the main aspects (i.e., topics) in the document; and (3) how to balance these two requests. Specially for the issue relevance, many traditional summarization techniques assume that there is independent relevance between sentences, which may not hold in reality. In this paper, we go beyond this assumption and propose a novel Probabilistic-modeling Relevance, Coverage, and Novelty (PRCN) framework, which exploits a reference topic model incorporating user query for dependent relevance measurement. Along this line, topic coverage is also modeled under our framework. To further address the issues above, various sentence features regarding relevance and novelty are constructed as features, while moderate topic coverage are maintained through a greedy algorithm for topic balance. Finally, experiments on DUC2005 and DUC2006 datasets validate the effectiveness of the proposed method.