Latent semantic analysis and the construction of coherent extracts

Abstract

We describe a language-neutral automatic summarization system which aims to produce coherent extracts. It builds an initial extract composed solely of topic sentences, and then recursively fills in the topical lacunae by providing linking material between semantically dissimilar sentences. While experiments with human judges did not prove a statistically significant increase in textual coherence with the use of a latent semantic analysis module, we found a strong positive correlation between coherence and overall summary quality.