Toward a Gold Standard for Extractive Text Summarization

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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.
Alistair Kennedy, Stan Szpakowicz
Added 18 Jul 2010
Updated 18 Jul 2010
Type Conference
Year 2010
Where AI
Authors Alistair Kennedy, Stan Szpakowicz
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