Social Narrative Adaptation using Crowdsourcing

Abstract

In this paper we present SNACS, a novel method for creating Social
Narratives that can be Adapted using information from Crowdsourcing.
Previous methods for automatic narrative generation require that the
primary author explicitly detail nearly all parts of the story,
including details about the narrative. This is also the case for
narratives within computer games, educational tools and Embodied
Conversational Agents (ECA). While such narratives are well written,
they clearly require significant time and cost overheads. SNACS is a
hybrid narrative generation method that merges partially formed
preexisting narratives with new input from crowdsourcing techniques.
We compared the automatically generated narratives with those that
were created solely by people, and with those that were generated
semi-automatically by a state-of-the-art narrative planner. We
empirically found that SNACS was effective as people found narratives
generated by SNACS to be as realistic and consistent as those manually
created by the people or the narrative planner. Yet, the automatically
generated narratives were created with much lower time overheads and
were significantly more diversified, making them more suitable for
many applications.