Toward a gold standard for extractive text summarization

Authors:
Alistair Kennedy;Stan Szpakowicz
Affiliations:
School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada;,School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada
Venue:
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Year:
2010

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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.