  Referential gestures, or as termed in linguistics, deixis, are an essential
part of communication around data visualizations. Despite their importance,
such gestures are often overlooked when documenting data analysis meetings.
Transcripts, for instance, fail to capture gestures, and video recordings may
not adequately capture or emphasize them. We introduce a novel method for
documenting collaborative data meetings that treats deixis as a first-class
citizen. Our proposed framework captures cursor-based gestural data along with
audio and converts them into interactive documents. The framework leverages a
large language model to identify word correspondences with gestures. These
identified references are used to create context-based annotations in the
resulting interactive document. We assess the effectiveness of our proposed
method through a user study, finding that participants preferred our automated
interactive documentation over recordings, transcripts, and manual note-taking.
Furthermore, we derive a preliminary taxonomy of cursor-based deictic gestures
from participant actions during the study. This taxonomy offers further
opportunities for better utilizing cursor-based deixis in collaborative data
analysis scenarios.
