Abstract
Search users rely on result captions including titles, snippets, and URLs to decide whether they should read and click a particular result or not. Snippet usually serves as a query-dependent summary of its corresponding landing page and is therefore treated as one of the most important factors in search interaction process. Although there exist many efforts in improving snippet generation algorithms and incorporating more powerful interaction functions into snippets, little attention is paid to the effect of text highlighting in user behaviors. The highlighting of query terms in search snippets has been regarded as a matter of course and whether there exists a better way in snippet text highlighting remains uninvestigated. In this paper, we try to find out whether the default strategy of highlighting query terms employed by most commercial search engines is the best for search users. Through carefully designed experiments, we show that the retrieval efficiency can be affected by different term-highlighting strategies without changes in snippet contents. We also propose an automatic method which adopts CRF to learn to highlight terms based on word embedding, Wikipedia, and snippet content information. Experimental results show that the proposed method could predict highlighted terms selected by crowd workers with moderate performance.
Highlights
For most commercial search engines, many novel forms of search results have been incorporated into result lists, the major parts of results are still in the traditional form which contains title, snippets, and URLs
Search users rely on this caption information to decide whether they should click on the result and read the content of the landing page. erefore, the organization of result caption information, especially the generation of snippets, is closely related with user’s search interaction process and has been one of the major concerns in search engine UI studies [7,8,9,10,11,12]
To study the effect of highlighted snippet terms in user behavior, we propose three different highlighting strategies besides the original query terms highlighting method based on the highlighted terms list obtained by crowdsourcing process: (i) Original Highlighting Strategy (S1): is is the original query terms highlighting strategy adopted by Google and other commercial search engines. is strategy considers that the query word is a correlation indicator. e more query words, the higher the correlation
Summary
For most commercial search engines, many novel forms of search results (e.g., verticals [1, 2], cards [3, 4], knowledge graphs [5], and direct answers [6]) have been incorporated into result lists, the major parts of results are still in the traditional form which contains title, snippets, and URLs. Erefore, the organization of result caption information, especially the generation of snippets, is closely related with user’s search interaction process and has been one of the major concerns in search engine UI studies [7,8,9,10,11,12]. Most of these existing studies investigate the appropriate presentation styles of snippets for search users such as length [7, 13] and readability [8, 14]. According to both manual experimental results [16] and eyetracking studies [10], query term highlighting can help draw
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