Word Clouds of Multiple Search Results

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.