IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Making Topic-Specific Report and Multimodal Presentation Automatically by Mining the Web Resources
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
SlideSeer: a digital library of aligned document and presentation pairs
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Developing learning strategies for topic-based summarization
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Investigating automatic alignment methods for slide generation from academic papers
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Automatic slide presentation from semantically annotated documents
CorefApp '99 Proceedings of the Workshop on Coreference and its Applications
A scalable global model for summarization
ILP '09 Proceedings of the Workshop on Integer Linear Programming for Natural Langauge Processing
A study of global inference algorithms in multi-document summarization
ECIR'07 Proceedings of the 29th European conference on IR research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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 slide generation based on discourse structure analysis
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
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
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In this paper, we investigate a very challenging task of automatically generating presentation slides for academic papers. The generated presentation slides can be used as drafts to help the presenters prepare their formal slides in a quicker way. A novel system called PPSGen is proposed to address this task. It first employs regression methods to learn the importance of the sentences in an academic paper, and then exploits the integer linear programming (ILP) method to generate well-structured slides by selecting and aligning key phrases and sentences. Evaluation results on a test set of 200 pairs of papers and slides collected on the web demonstrate that our proposed PPSGen system can generate slides with better quality. A user study is also illustrated to show that PPSGen has a few evident advantages over baseline methods.