Summarizing Similarities and Differences Among Related Documents
Information Retrieval
Summarizing scientific articles: experiments with relevance and rhetorical status
Computational Linguistics - Summarization
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
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
Counter-training in discovery of semantic patterns
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Probabilistic text structuring: experiments with sentence ordering
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
A semantic approach to IE pattern induction
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Bayesian query-focused summarization
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Automatic Evaluation of Information Ordering: Kendall's Tau
Computational Linguistics
Beyond SumBasic: Task-focused summarization with sentence simplification and lexical expansion
Information Processing and Management: an International Journal
Modeling local coherence: An entity-based approach
Computational Linguistics
FastSum: fast and accurate query-based multi-document summarization
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Support vector machines for query-focused summarization trained and evaluated on pyramid data
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Widening the field of view of information extraction through sentential event recognition
Widening the field of view of information extraction through sentential event recognition
Who wrote what where: analyzing the content of human and automatic summaries
WASDGML '11 Proceedings of the Workshop on Automatic Summarization for Different Genres, Media, and Languages
Generating aspect-oriented multi-document summarization with event-aspect model
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Towards a unified approach based on affinity graph to various multi-document summarizations
ECDL'07 Proceedings of the 11th European conference on Research and Advanced Technology for Digital Libraries
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The TAC 2010 summarization track initiated a new task—aspect-guided summarization—that centers on textual aspects embodied as particular kinds of information of a text. We observe that aspect-guided summaries not only address highly specific user need, but also facilitate content-level coherence by using aspect information. In this article, we present a full-fledged approach to aspect-guided summarization with a focus on summary coherence. Our summarization approach depends on two prerequisite subtasks: recognizing aspect-bearing sentences in order to do sentence extraction, and modeling aspect-based coherence with an HMM model in order to predict a coherent sentence ordering. Using the manually annotated TAC 2010 and 2010 datasets, we validated the effectiveness of our proposed methods for those subtasks. Drawing on the empirical results, we proceed to develop an aspect-guided summarizer based on a simple but robust base summarizer. With sentence selection guided by aspect information, our system is one of the best on TAC 2011. With sentence ordering predicted by the aspect-based HMM model, the summaries achieve good coherence.