Extraction of characteristic facts and abstract generation

Abstract

This paper presents a formalization of the summary generation process, which takes a set of observed facts as input and generates its summary in the form of natural language text. Our assumption is that human summarization process consists of the following four phrases: (1) reconstructing the original semantic structure in his/her mind from observed facts, (2) extracting the characteristic facts from the reconstructed structure, (3) constructing the surface structure of text in the macroscopic view and finally (4) generating a natural language text from the surface structure. Our system generates newspaper-like texts in the domain of baseball games. The implementation is also briefly presented.