Contextual Fact Ranking and Its Applications in Table Synthesis and Compression

Modern search engines increasingly incorporate tabular content,
which consists of a set of entities each augmented with a small set
of facts. The facts can be obtained from multiple sources: an entity’s
knowledge base entry, the infobox on its Wikipedia page, or its row
within a WebTable. Crucially, the informativeness of a fact depends
not only on the entity but also the specific context (e.g., the query).
To the best of our knowledge, this paper is the first to study the

problem of contextual fact ranking: given some entities and a con-
text (i.e., succinct natural language description), identify the most

informative facts for the entities collectively within the context.
We propose to contextually rank the facts by exploiting deep

learning techniques. In particular, we develop pointwise and pair-
wise ranking models, using textual and statistical information for

the given entities and context derived from their sources. We en-
hance the models by incorporating entity type information from

an IsA (hypernym) database. We demonstrate that our approaches
achieve better performance than state-of-the-art baselines in terms
of MAP, NDCG, and recall. We further conduct user studies for two
specific applications of contextual fact ranking—table synthesis and
table compression—and show that our models can identify more
informative facts than the baselines.

• Data Mining and Modeling