Effective Anchoring of Multiview Narrative Generation

Humans inherently learn from and interact with multiple views of information, be it various modalities or languages. So, the expectations from contemporary and 21st-century technology are a testimony for the increasing need to model these multiview contexts better. Natural language generation plays a pivotal role in communicating these contexts in human-understandable languages. This thesis brings together both of these transformative technologies to make strides towards a long-standing dream of human-like multiview narrative generation. The critical challenge is identifying the natural-sounding properties of long-form texts and modeling them in tandem with visual contexts. This thesis presents anchors for grounding three such properties including content (relevance), structure (coherence), and surface form realization (expression), and anchors them with relevant visual contexts. These anchors also provide us with human interpretable handles for controlling these properties.