Artificial Intelligence - On connectionist symbol processing
Automatic condensation of electronic publications by sentence selection
Information Processing and Management: an International Journal - Special issue: summarizing text
OCELOT: a system for summarizing Web pages
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Applying summarization techniques for term selection in relevance feedback
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Cluster-based retrieval using language models
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Corpus structure, language models, and ad hoc information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Web page summarization using dynamic content
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Impedance coupling in content-targeted advertising
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Generating query substitutions
Proceedings of the 15th international conference on World Wide Web
Query rewriting using active learning for sponsored search
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Just-in-time contextual advertising
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A noisy-channel approach to contextual advertising
Proceedings of the 1st international workshop on Data mining and audience intelligence for advertising
Simrank++: query rewriting through link analysis of the click graph
Proceedings of the VLDB Endowment
Search advertising using web relevance feedback
Proceedings of the 17th ACM conference on Information and knowledge management
Context transfer in search advertising
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Language models based on semantic composition
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Proceedings of the VLDB Endowment
Categorization of display ads using image and landing page features
Proceedings of the Third Workshop on Large Scale Data Mining: Theory and Applications
Learning discriminative projections for text similarity measures
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Advertising Keywords Recommendation for Short-Text Web Pages Using Wikipedia
ACM Transactions on Intelligent Systems and Technology (TIST)
Sponsored search ad selection by keyword structure analysis
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages
Proceedings of the 22nd international conference on World Wide Web
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We explore the use of the landing page content in sponsored search ad selection. Specifically, we compare the use of the ad's intrinsic content to augmenting the ad with the whole, or parts, of the landing page. We explore two types of extractive summarization techniques to select useful regions from the landing pages: out-of-context and in-context methods. Out-of-context methods select salient regions from the landing page by analyzing the content alone, without taking into account the ad associated with the landing page. In-context methods use the ad context (including its title, creative, and bid phrases) to help identify regions of the landing page that should be used by the ad selection engine. In addition, we introduce a simple yet effective unsupervised algorithm to enrich the ad context to further improve the ad selection. Experimental evaluation confirms that the use of landing pages can significantly improve the quality of ad selection. We also find that our extractive summarization techniques reduce the size of landing pages substantially, while retaining or even improving the performance of ad retrieval over the method that utilize the entire landing page.