  Relevant language describing trends in data can be useful for generating
summaries to help with readers' takeaways. However, the language employed in
these often template-generated summaries tends to be simple, ranging from
describing simple statistical information (e.g., extrema and trends) without
additional context and richer language to provide actionable insights. Recent
advances in Large Language Models (LLMs) have shown promising capabilities in
capturing subtle nuances in language when describing information. This workshop
paper specifically explores how LLMs can provide more actionable insights when
describing trends by focusing on three dimensions of analytical narrative
structure: semantic, rhetorical, and pragmatic. Building on prior research that
examines visual and linguistic signatures for univariate line charts, we
examine how LLMs can further leverage the semantic dimension of analytical
narratives using quantified semantics to describe shapes in trends as people
intuitively view them. These semantic descriptions help convey insights in a
way that leads to a pragmatic outcome, i.e., a call to action, persuasion,
warning vs. alert, and situational awareness. Finally, we identify rhetorical
implications for how well these generated narratives align with the perceived
shape of the data, thereby empowering users to make informed decisions and take
meaningful actions based on these data insights.
