  Politicians often have underlying agendas when reacting to events. Arguments
in contexts of various events reflect a fairly consistent set of agendas for a
given entity. In spite of recent advances in Pretrained Language Models (PLMs),
those text representations are not designed to capture such nuanced patterns.
In this paper, we propose a Compositional Reader model consisting of encoder
and composer modules, that attempts to capture and leverage such information to
generate more effective representations for entities, issues, and events. These
representations are contextualized by tweets, press releases, issues, news
articles, and participating entities. Our model can process several documents
at once and generate composed representations for multiple entities over
several issues or events. Via qualitative and quantitative empirical analysis,
we show that these representations are meaningful and effective.
