  News articles, image captions, product reviews and many other texts mention
people and organizations whose name recognition could vary for different
audiences. In such cases, background information about the named entities could
be provided in the form of an appositive noun phrase, either written by a human
or generated automatically. We expand on the previous work in appositive
generation with a new, more realistic, end-to-end definition of the task,
instantiated by a dataset that spans four languages (English, Spanish, German
and Polish), two entity types (person and organization) and two domains
(Wikipedia and News). We carry out an extensive analysis of the data and the
task, pointing to the various modeling challenges it poses. The results we
obtain with standard language generation methods show that the task is indeed
non-trivial, and leaves plenty of room for improvement.
