A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Using hidden Markov modeling to decompose human-written summaries
Computational Linguistics - Summarization
Sentence reduction for automatic text summarization
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Cut and paste based text summarization
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
A noisy-channel model for document compression
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Headline generation based on statistical translation
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Improving summarization performance by sentence compression: a pilot study
AsianIR '03 Proceedings of the sixth international workshop on Information retrieval with Asian languages - Volume 11
Hedge Trimmer: a parse-and-trim approach to headline generation
HLT-NAACL-DUC '03 Proceedings of the HLT-NAACL 03 on Text summarization workshop - Volume 5
Abstractive headline generation using WIDL-expressions
Information Processing and Management: an International Journal
Multi-candidate reduction: Sentence compression as a tool for document summarization tasks
Information Processing and Management: an International Journal
Practical structured learning techniques for natural language processing
Practical structured learning techniques for natural language processing
Sentence compression beyond word deletion
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Summarization with a joint model for sentence extraction and compression
ILP '09 Proceedings of the Workshop on Integer Linear Programming for Natural Langauge Processing
Global inference for sentence compression an integer linear programming approach
Journal of Artificial Intelligence Research
Quasi-synchronous grammars: alignment by soft projection of syntactic dependencies
StatMT '06 Proceedings of the Workshop on Statistical Machine Translation
Paraphrase identification as probabilistic quasi-synchronous recognition
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
Application-driven statistical paraphrase generation
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 2 - Volume 2
Parser adaptation and projection with quasi-synchronous grammar features
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Topic models for image annotation and text illustration
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Automatic generation of story highlights
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
How many words is a picture worth? Automatic caption generation for news images
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Exploiting separability in large-scale linear support vector machine training
Computational Optimization and Applications
Optimal and syntactically-informed decoding for monolingual phrase-based alignment
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Towards strict sentence intersection: decoding and evaluation strategies
MTTG '11 Proceedings of the Workshop on Monolingual Text-To-Text Generation
Paraphrastic sentence compression with a character-based metric: tightening without deletion
MTTG '11 Proceedings of the Workshop on Monolingual Text-To-Text Generation
Evaluating sentence compression: pitfalls and suggested remedies
MTTG '11 Proceedings of the Workshop on Monolingual Text-To-Text Generation
Learning to simplify sentences with quasi-synchronous grammar and integer programming
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Collective generation of natural image descriptions
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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
Expert Systems with Applications: An International Journal
Comparing resources for spanish lexical simplification
SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
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The task of selecting information and rendering it appropriately appears in multiple contexts in summarization. In this paper we present a model that simultaneously optimizes selection and rendering preferences. The model operates over a phrase-based representation of the source document which we obtain by merging PCFG parse trees and dependency graphs. Selection preferences for individual phrases are learned discriminatively, while a quasi-synchronous grammar (Smith and Eisner, 2006) captures rendering preferences such as paraphrases and compressions. Based on an integer linear programming formulation, the model learns to generate summaries that satisfy both types of preferences, while ensuring that length, topic coverage and grammar constraints are met. Experiments on headline and image caption generation show that our method obtains state-of-the-art performance using essentially the same model for both tasks without any major modifications.