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 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.

• Type: Conference
• Year: 2010
• Where: AI
• Authors: Alistair Kennedy, Stan Szpakowicz
• Keywords: AI 2010, Artificial Intelligence, Extractive Summarization, Extractive Text Summarization, Relevant Sentences