Scatter/gather browsing communicates the topic structure of a very large text collection
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Advantages of query biased summaries in information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Finding relevant documents using top ranking sentences: an evaluation of two alternative schemes
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Biterm language models for document retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Inferring hierarchical descriptions
Proceedings of the eleventh international conference on Information and knowledge management
DOM-based content extraction of HTML documents
WWW '03 Proceedings of the 12th international conference on World Wide Web
Parsimonious language models for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Keyphrase extraction-based query expansion in digital libraries
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
Getting our head in the clouds: toward evaluation studies of tagclouds
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Tag clouds for summarizing web search results
Proceedings of the 16th international conference on World Wide Web
An assessment of tag presentation techniques
Proceedings of the 16th international conference on World Wide Web
Seeing things in the clouds: the effect of visual features on tag cloud selections
Proceedings of the nineteenth ACM conference on Hypertext and hypermedia
Term clouds as surrogates for user generated speech
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Enhancing cluster labeling using wikipedia
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Domain-specific keyphrase extraction
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Coherent keyphrase extraction via web mining
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Entity ranking using Wikipedia as a pivot
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
On the selection of tags for tag clouds
Proceedings of the fourth ACM international conference on Web search and data mining
How different are language models andword clouds?
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
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Search engine result pages (SERPs) are known as the most expensive real estate on the planet. Most queries yield millions of organic search results, yet searchers seldom look beyond the first handful of results. To make things worse, different searchers with different query intents may issue the exact same query. An alternative to showing individual web pages summarized by snippets is to represent whole group of results. In this paper we investigate if we can use word clouds to summarize groups of documents, e.g. to give a preview of the next SERP, or clusters of topically related documents. We experiment with three word cloud generation methods (full-text, query biased and anchor text based clouds) and evaluate them in a user study. Our findings are: First, biasing the cloud towards the query does not lead to test persons better distinguishing relevance and topic of the search results, but test persons prefer them because differences between the clouds are emphasized. Second, anchor text clouds are to be preferred over full-text clouds. Anchor text contains less noisy words than the full text of documents. Third, we obtain moderately positive results on the relation between the selected world clouds and the underlying search results: there is exact correspondence in 70% of the subtopic matching judgments and in 60% of the relevance assessment judgments. Our initial experiments open up new possibilities to have SERPs reflect a far larger number of results by using word clouds to summarize groups of search results.