A summariser based on human memory limitations and lexical competition

Abstract

Kintsch and van Dijk proposed a model of human comprehension and summarisation which is based on the idea of processing propositions on a sentence-by-sentence basis, detecting argument overlap, and creating a summary on the basis of the best connected propositions. We present an implementation of that model, which gets around the problem of identifying concepts in text by applying coreference resolution, named entity detection, and semantic similarity detection, implemented as a two-step competition. We evaluate the resulting summariser against two commonly used extractive summarisers using ROUGE, with encouraging results. © 2014 Association for Computational Linguistics.