The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Generating comparative summaries of contradictory opinions in text
Proceedings of the 18th ACM conference on Information and knowledge management
Mining user reviews: from specification to summarization
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference 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
Summarizing contrastive viewpoints in opinionated text
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Multi-document summarization via the minimum dominating set
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Efficient confident search in large review corpora
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Summarizing the differences in multilingual news
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Lightweight contrastive summarization for news comment mining
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
CONSENTO: a new framework for opinion based entity search and summarization
Proceedings of the 21st ACM international conference on Information and knowledge management
| Hi-index | 0.00 |
To facilitate direct comparisons between different products, we present an approach to constructing short and comparative summaries based on product reviews. In particular, the user can view automatically aligned pairs of snippets describing reviewers' opinions on different features (also selected automatically by our approach) for two selected products. We propose a submodular objective function that avoids redundancy, that is efficient to optimize, and that aligns the snippets into pairs. Snippets are chosen from product reviews and thus easy to obtain. In our experiments, we show that the method constructs qualitatively good summaries, and that it can be tuned via supervised learning.