This chapter examines the nature of semantic relations and their main applications in
information science. The nature and types of semantic relations are discussed from the …

A key problem in text summarization is finding a salience function which determines what
information in the source should be included in the summary. This paper describes the use …

Automatic Indexing and Abstracting of Document Texts summarizes the latest techniques of
automatic indexing and abstracting, and the results of their application. It also places the …

Summarization entails analysis of source material, selection of key information,
condensation of this, and generation of a compact summary form. While there have been …

We describe a scalable summarization system which takes advantage of robust NLP
technology such as corpus-based statlshcal NLP techmques, information extractmn and …

Engineering and research teams often develop new products and technologies by referring
to inventions described in patent databases. Efficient patent analysis builds R&D …

(57) ABSTRACT A computer method for preparing a Summary String from a Source
document of encoded text. The method comprises comparing a training Set of encoded text …

Sentence extraction is a widely adopted text summarization technique where the most
important sentences are extracted from document (s) and presented as a summary. The first …

Four working steps taken from a comprehensive empirical model of expert abstracting are
studied in order to prepare an explorative implementation of a simulation model. It aims at …

SimSum (Simulation of Summarizing) simulates 20 real-world working steps of expert
summarizers. It presents an empirically founded cognitive model of summarizing and …