Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Extracting knowledge from evaluative text
Proceedings of the 3rd international conference on Knowledge capture
Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
A formal model for information selection in multi-sentence text extraction
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Identifying sources of opinions with conditional random fields and extraction patterns
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Modeling online reviews with multi-grain topic models
Proceedings of the 17th international conference on World Wide Web
Multi-document summarization by sentence extraction
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLP Workshop on Automatic Summarization
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Mining comparative sentences and relations
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Topic identification for fine-grained opinion analysis
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
INLG '08 Proceedings of the Fifth International Natural Language Generation Conference
A study of global inference algorithms in multi-document summarization
ECIR'07 Proceedings of the 29th European conference on IR research
Pulse: mining customer opinions from free text
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Query-based opinion summarization for legal blog entries
Proceedings of the 12th International Conference on Artificial Intelligence and Law
Contrastive summarization: an experiment with consumer reviews
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Latent aspect rating analysis on review text data: a rating regression approach
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
The viability of web-derived polarity lexicons
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
An unsupervised aspect-sentiment model for online reviews
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Optimizing informativeness and readability for sentiment summarization
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
The bag-of-opinions method for review rating prediction from sparse text patterns
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Aspect-based extractive summarization of online reviews
Proceedings of the 2011 ACM Symposium on Applied Computing
Aspect ranking: identifying important product aspects from online consumer reviews
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Text summarisation in progress: a literature review
Artificial Intelligence Review
A large-scale sentiment analysis for Yahoo! answers
Proceedings of the fifth ACM international conference on Web search and data mining
Opinion summarization of web comments
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
An effective approach for topic-specific opinion summarization
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Survey on mining subjective data on the web
Data Mining and Knowledge Discovery
Review summarization based on linguistic knowledge
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications
Information Retrieval in the Commentsphere
ACM Transactions on Intelligent Systems and Technology (TIST)
Challenges and solutions in the opinion summarization of user-generated content
Journal of Intelligent Information Systems
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Graph-based informative-sentence selection for opinion summarization
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Hidden factors and hidden topics: understanding rating dimensions with review text
Proceedings of the 7th ACM conference on Recommender systems
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We present the results of a large-scale, end-to-end human evaluation of various sentiment summarization models. The evaluation shows that users have a strong preference for summarizers that model sentiment over non-sentiment baselines, but have no broad overall preference between any of the sentiment-based models. However, an analysis of the human judgments suggests that there are identifiable situations where one summarizer is generally preferred over the others. We exploit this fact to build a new summarizer by training a ranking SVM model over the set of human preference judgments that were collected during the evaluation, which results in a 30% relative reduction in error over the previous best summarizer.