  Longitudinal corpora like newspaper archives are of immense value to
historical research, and time as an important factor for historians strongly
influences their search behaviour in these archives. While searching for
articles published over time, a key preference is to retrieve documents which
cover the important aspects from important points in time which is different
from standard search behavior. To support this search strategy, we introduce
the notion of a Historical Query Intent to explicitly model a historian's
search task and define an aspect-time diversification problem over news
archives.
  We present a novel algorithm, HistDiv, that explicitly models the aspects and
important time windows based on a historian's information seeking behavior. By
incorporating temporal priors based on publication times and temporal
expressions, we diversify both on the aspect and temporal dimensions. We test
our methods by constructing a test collection based on The New York Times
  Collection with a workload of 30 queries of historical intent assessed
manually. We find that HistDiv outperforms all competitors in subtopic recall
with a slight loss in precision. We also present results of a qualitative user
study to determine wether this drop in precision is detrimental to user
experience. Our results show that users still preferred HistDiv's ranking.
