Three methods merge lists of results from topically-separated queries: SimM only considers their similarity with the fragment; Round-robin picks the best document in each list; and our proposal, DivM, considers the diversity and importance of topics.
a new approach will be proposed, and will be compared in particular to a version of the explicit diversification approach (Santos et al., 2010) adapted to our problem.