Non-Monotonic Reasoning and Story Comprehension

TL;DR

This paper presents a reasoning framework for story comprehension that integrates explicit narrative information with implicit world knowledge using argumentation, supported by empirical studies on human understanding variability.

Contribution

It introduces a novel argumentation-based reasoning framework for story comprehension that combines explicit and implicit knowledge, inspired by psychology and AI.

Findings

Successfully captures human variability in story understanding

Integrates explicit narrative with implicit world knowledge

Demonstrates effectiveness through prototype experiments

Abstract

This paper develops a Reasoning about Actions and Change framework integrated with Default Reasoning, suitable as a Knowledge Representation and Reasoning framework for Story Comprehension. The proposed framework, which is guided strongly by existing knowhow from the Psychology of Reading and Comprehension, is based on the theory of argumentation from AI. It uses argumentation to capture appropriate solutions to the frame, ramification and qualification problems and generalizations of these problems required for text comprehension. In this first part of the study the work concentrates on the central problem of integration (or elaboration) of the explicit information from the narrative in the text with the implicit (in the readers mind) common sense world knowledge pertaining to the topic(s) of the story given in the text. We also report on our empirical efforts to gather background common sense world knowledge used by humans when reading a story and to evaluate, through a prototype system, the ability of our approach to capture both the majority and the variability of understanding of a story by the human readers in the experiments.