Scalable generation of texts using causal and temporal expansions of sentences

Abstract

This paper presents a exible bottom-up process to incrementally generate several versions of the same text, building up the core text from its kernel version into other versions varying of the levels of details. We devise a method for identifying the question/answer relations holding between the propositions of a text, we give rules for characterizing the kernel version of a text, and we provide a procedure, based on causal and temporal expansions of sentences, which distinguishes semantically these levels of details according to their importance. This is based on the assumption that we have a stock of information from the interpreter's knowledge base available. The sentence expansion operation is formally defined according to three principles: (1) the kernel principle ensures to obtain the gist information; (2) the expansion principle defines an incremental augmentation of a text; and (3) the subsume principle defines an importance-based order among the possible details of the information. The system developed allows users to generate in a follow-up way their own text version which meets their expectations and their demands expressed as questions about the text under consideration.