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Abstract:
Given an entity represented by a single node q in semantic knowledge graph D,
the Graphical Entity Summarisation problem (GES) consists in selecting out of D
a very small surrounding graph S that constitutes a generic summary of the
information concerning the entity q with given limit on size of S. This article
concerns the role of diversity in this quite novel problem. It gives an
overview of the diversity concept in information retrieval, and proposes how to
adapt it to GES. A measure of diversity for GES, called ALC, is defined and two
algorithms presented, baseline, diversity-oblivious PRECIS and diversity-aware
DIVERSUM. A reported experiment shows that DIVERSUM actually achieves higher
values of the ALC diversity measure than PRECIS. Next, an objective evaluation
experiment demonstrates that diversity-aware algorithm is superior to the
diversity-oblivious one in terms of fact selection. More precisely, DIVERSUM
clearly achieves higher recall than PRECIS on ground truth reference entity
summaries extracted from Wikipedia. We also report another intrinsic
experiment, in which the output of diversity-aware algorithm is significantly
preferred by human expert evaluators. Importantly, the user feedback clearly
indicates that the notion of diversity is the key reason for the preference. In
addition, the experiment is repeated twice on an anonymous sample of broad
population of Internet users by means of a crowd-sourcing platform, that
further confirms the results mentioned above.
