Extractive Summarization under Strict Length Constraints

Abstract

In this paper we report a comparison of various techniques for single-document extractive summarization under strict length budgets, which is a common commercial use case (e.g. summarization of news articles by news aggregators). We show that, evaluated using ROUGE, numerous algorithms from the literature fail to beat a simple lead-based baseline for this task. However, a supervised approach with lightweight and efficient features improves over the lead-based baseline. Additional human evaluation demonstrates that the supervised approach also performs competitively with a commercial system that uses more sophisticated features.

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Reproductions