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
SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
A new approach to unsupervised text summarization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Conceptual Knowledge Discovery in Databases Using Formal Concept Analysis Methods
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A Lattice Machine Approach to Automated Casebase Design: Marrying Lazy and Eager Learning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Lexical cohesion computed by thesaural relations as an indicator of the structure of text
Computational Linguistics
Distribution of content words and phrases in text and language modelling
Natural Language Engineering
Proceedings of the 13th international conference on World Wide Web
The automated acquisition of topic signatures for text summarization
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
On the detection of semantic concepts at TRECVID
Proceedings of the 12th annual ACM international conference on Multimedia
Centroid-based summarization of multiple documents
Information Processing and Management: an International Journal
Topic themes for multi-document summarization
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
An efficient text summarizer using lexical chains
INLG '00 Proceedings of the first international conference on Natural language generation - Volume 14
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Experiments in multidocument summarization
HLT '02 Proceedings of the second international conference on Human Language Technology Research
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
GA, MR, FFNN, PNN and GMM based models for automatic text summarization
Computer Speech and Language
Text summarization model based on maximum coverage problem and its variant
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Summarizing definition from Wikipedia
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts of ACL 2011
Exploring hypergraph-based semi-supervised ranking for query-oriented summarization
Information Sciences: an International Journal
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We argue that the quality of a summary can be evaluated based on how many concepts in the original document(s) that can be preserved after summarization. Here, a concept refers to an abstract or concrete entity or its action often expressed by diverse terms in text. Summary generation can thus be considered as an optimization problem of selecting a set of sentences with minimal answer loss. In this paper, we propose a document concept lattice that indexes the hierarchy of local topics tied to a set of frequent concepts and the corresponding sentences containing these topics. The local topics will specify the promising sub-spaces related to the selected concepts and sentences. Based on this lattice, the summary is an optimized selection of a set of distinct and salient local topics that lead to maximal coverage of concepts with the given number of sentences. Our summarizer based on the concept lattice has demonstrated competitive performance in Document Understanding Conference 2005 and 2006 evaluations as well as follow-on tests.