On the Computation of Point of View

Abstract

Previous work in AI story understanding has largely been used to build tools which can summarize stories and categorize them according to the events they describe (e.g., the technologies developed for the Message Understanding Conferences). These sorts of technologies are built around the assumptions that (1) events reported as facts in news stories should be understood as facts; (2) the style of a story, i.e., the way in which a story is told, is not of interest; and, (3) the source of a story should not influence its analysis. These assumptions are obviously unrealistic. Everyone knows that one should not believe everything in the news. But, by making these simplifying assumptions most existing story understanding systems function as gullible readers. The focus of my current research is to build a less gullible story understander by encoding in it a means to recognize point of view. The techniques that I am developing will be useful, not only for information retrieval tasks which demand a search for credible stories, but also in future entertainment technologies which will be capable of finding and then assembling together into a unified presentation a set of texts or video clips to tell a story from an ensemble of points of view.