Approximation algorithms for NP-hard problems
Statistics-Based Summarization - Step One: Sentence Compression
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Supervised and unsupervised learning for sentence compression
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Unsupervised Multilingual Sentence Boundary Detection
Computational Linguistics
Topic-focused multi-document summarization using an approximate oracle score
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Solution of Large Quadratic Knapsack Problems Through Aggressive Reduction
INFORMS Journal on Computing
Multi-document summarization by sentence extraction
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLP Workshop on Automatic Summarization
Learning and inference for hierarchically split PCFGs
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Global inference for sentence compression an integer linear programming approach
Journal of Artificial Intelligence Research
Multi-document summarization by maximizing informative content-words
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A study of global inference algorithms in multi-document summarization
ECIR'07 Proceedings of the 29th European conference on IR research
Multiple documents summarization based on genetic algorithm
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Metadata-aware measures for answer summarization in community Question Answering
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
A comparative study on ranking and selection strategies for multi-document summarization
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts of ACL 2011
Jointly learning to extract and compress
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Automatic assessment of coverage quality in intelligence reports
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Generating aspect-oriented multi-document summarization with event-aspect model
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Using concept-level random walk model and global inference algorithm for answer summarization
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Large-margin learning of submodular summarization models
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Multiple aspect summarization using integer linear programming
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Sub-sentence extraction based on combinatorial optimization
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Automatically assessing machine summary content without a gold standard
Computational Linguistics
PPSGen: learning to generate presentation slides for academic papers
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We present an Integer Linear Program for exact inference under a maximum coverage model for automatic summarization. We compare our model, which operates at the sub-sentence or "concept-level, to a sentence-level model, previously solved with an ILP. Our model scales more efficiently to larger problems because it does not require a quadratic number of variables to address redundancy in pairs of selected sentences. We also show how to include sentence compression in the ILP formulation, which has the desirable property of performing compression and sentence selection simultaneously. The resulting system performs at least as well as the best systems participating in the recent Text Analysis Conference, as judged by a variety of automatic and manual content-based metrics.