ABSTRACT GENERATION FOR NEWSPAPER ARITCLES

Abstract

Extracting necessary information is hard and time consuming in the information-oriented society. An abstract generation system for newspaper that represents a large volume of information is vital. This paper presents an experimental system devoloped for abstracting newspaper articles on traffic accidents without applying complicated natural language processing techniques. The level of abstraction can be selected by the user. The user saves time significantly by excluding unwanted information in the article.