The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Understanding the Interpretability of Search Result Summaries
Abstract
We examine the interpretability of search results in current web search engines through a lab user study. Particularly, we evaluate search result summary as an interpretable technique that informs users why the system retrieves a result and to which extent the result is useful. We collected judgments about 1,252 search results from 40 users in 160 sessions. Experimental results indicate that the interpretability of a search result summary is a salient factor influencing users' click decisions. Users are less likely to click on a result link if they do not understand why it was retrieved (low transparency) or cannot assess if the result would be useful based on the summary (low assessability). Our findings suggest it is crucial to improve the interpretability of search result summaries and develop better techniques to explain retrieved results to search engine users.
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“Research in non-academic search shows that summaries' assessability, i.e., users' ability to tell whether results are useful without opening links, significantly influences which results users choose to explore (Mi & Jiang, 2019).”
From AI Overviews in Academic Search: Evaluating AI-generated Summaries of Search Results in a Domain-specific Search Engine (2026) · cites this paper as Mi & Jiang, 2019
In the citing paper
“The effectiveness of these summaries is also tied to their interpretability, as search results whose summaries are more transparent (regarding why a result was retrieved) and assessable (easy to tell if a result will be useful) are more likely to be clicked (Mi & Jiang, 2019).”
From AI Overviews in Academic Search: Evaluating AI-generated Summaries of Search Results in a Domain-specific Search Engine (2026) · cites this paper as Mi & Jiang, 2019
Citations
Cites
Accurately interpreting clickthrough data as implicit feedback
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Cited by
Sorted by citations
Schott, K., Silva, K., Frommholz, I., Mayr, P., Kern, D., & Hienert, D. (2026) · arXiv · doi:10.48550/arxiv.2607.03421
Metadata
- Authors
- Mi, S., & Jiang, J.
- Affiliations
- Virginia Tech, Blacksburg, VA, USA
- Venue
- Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2019-07-18
- Pages
- 989-992
- Cited by
- 5
- References
- 1
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