Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Micropinion generation: an unsupervised approach to generating ultra-concise summaries of opinions
Proceedings of the 21st international conference on World Wide Web
FindiLike: preference driven entity search
Proceedings of the 21st international conference companion on World Wide Web
Information Retrieval
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We describe a novel preference-driven search engine (FindiLike) which allows users to find entities of interest based on preferences and also allows users to digest opinions about the retrieved entities easily. FindiLike leverages large amounts of online reviews about various entities, and ranks entities based on how well their associated reviews match a user's preference query (expressed in keywords). FindiLike then uses abstractive summarization techniques to generate concise opinion summaries to enable users to digest the opinions about an entity. We discuss how the system can be extended to support in situ evaluation of two interesting new tasks, i.e., opinion-based entity ranking and abstractive summarization of opinions. The system is currently supporting hotel search and being extended to support in situ evaluation of these two tasks. We will demonstrate the system in the domain of hotel search and show how in situ evaluation can be supported through natural user interaction with the system.