Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
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
Topic modeling: beyond bag-of-words
ICML '06 Proceedings of the 23rd international conference on Machine learning
The Pyramid Method: Incorporating human content selection variation in summarization evaluation
ACM Transactions on Speech and Language Processing (TSLP)
Studying the history of ideas using topic models
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Multi-document summarization using sentence-based topic models
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Automatically evaluating content selection in summarization without human models
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
On smoothing and inference for topic models
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
ImpactWheel: Visual Analysis of the Impact of Online News
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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In the field of multi-document summarization, the Pyramid method has become an important approach for evaluating machine-generated summaries. The method is based on the manual annotation of text spans with the same meaning in a set of human model summaries. In this paper, we present an unsupervised, probabilistic topic modeling approach for automatically identifying such semantically similar text spans. Our approach reveals some of the structure of model summaries and identifies topics that are good approximations of the Summary Content Units (SCU) used in the Pyramid method. Our results show that the topic model identifies topic-sentence associations that correspond to the contributors of SCUs, suggesting that the topic modeling approach can generate a viable set of candidate SCUs for facilitating the creation of Pyramids.