Toward a gold standard for extractive text summarization

Abstract

Extractive text summarization is the process of selecting relevant sentences from a collection of documents, perhaps only a single document, and arranging such sentences in a purposeful way to form a summary of this collection. The question arises just how good extractive summarization can ever be. Without generating language to express the gist of a text - its abstract - can we expect to make summaries which are both readable and informative? In search for an answer, we employed a corpus partially labelled with Summary Content Units: snippets which convey the main ideas in the document collection. Starting from this corpus, we created SCU-optimal summaries for extractive summarization. We support the claim of optimality with a series of experiments. © 2010 Springer-Verlag Berlin Heidelberg.