  News captioning aims to describe an image with its news article body as
input. It greatly relies on a set of detected named entities, including
real-world people, organizations, and places. This paper exploits commonsense
knowledge to understand named entities for news captioning. By ``understand'',
we mean correlating the news content with common sense in the wild, which helps
an agent to 1) distinguish semantically similar named entities and 2) describe
named entities using words outside of training corpora. Our approach consists
of three modules: (a) Filter Module aims to clarify the common sense concerning
a named entity from two aspects: what does it mean? and what is it related to?,
which divide the common sense into explanatory knowledge and relevant
knowledge, respectively. (b) Distinguish Module aggregates explanatory
knowledge from node-degree, dependency, and distinguish three aspects to
distinguish semantically similar named entities. (c) Enrich Module attaches
relevant knowledge to named entities to enrich the entity description by
commonsense information (e.g., identity and social position). Finally, the
probability distributions from both modules are integrated to generate the news
captions. Extensive experiments on two challenging datasets (i.e., GoodNews and
NYTimes) demonstrate the superiority of our method. Ablation studies and
visualization further validate its effectiveness in understanding named
entities.
