Abstract: Summarization of an input story can be accomplished through identification of causal relationships, both explicit and implicit. The input story is transformed into an interpretation sequence, using syntactic cues, and common sense knowledge of an average reader. The interpretation sequence is a time ordered semantic representation of the input story, comprised of story elements. The common sense knowledge includes inference rules, which, from story elements already present, can add additional story elements to the interpretation sequence. Application of inference rules, based on type, can be prioritized. Summarization of the interpretation sequence can be accomplished by the selection of explicit story elements, according to a connection-based strategy, or a concept-based strategy. Regarding a concept-based strategy, metrics can be applied, to select the concepts for contra-causal searching of the interpretation sequence.

Abstract: Summarization of an input story can be accomplished through identification of causal relationships, both explicit and implicit. The input story is transformed into an interpretation sequence, using syntactic cues, and common sense knowledge of an average reader. The interpretation sequence is a time ordered semantic representation of the input story, comprised of story elements. The common sense knowledge includes inference rules, which, from story elements already present, can add additional story elements to the interpretation sequence. Application of inference rules, based on type, can be prioritized. Summarization of the interpretation sequence can be accomplished by the selection of explicit story elements, according to a connection-based strategy, or a concept-based strategy. Regarding a concept-based strategy, metrics can be applied, to select the concepts for contra-causal searching of the interpretation sequence.