Summarization with Precise Length Control

• URL: http://arxiv.org/abs/2305.05171v1
• Date: Tue, 9 May 2023 04:45:24 GMT
• Title: Summarization with Precise Length Control
• Authors: Lesly Miculicich, Yujia Xie, Song Wang, Pengcheng He
• Abstract summary: We present a framework to generate summaries with precisely the specified number of tokens or sentences. We jointly train the models to predict the lengths, so our model can generate summaries with optimal length.
• Score: 23.688834410051
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many applications of text generation such as summarization benefit from accurately controlling the text length. Existing approaches on length-controlled summarization either result in degraded performance or can only control the length approximately. In this work, we present a framework to generate summaries with precisely the specified number of tokens or sentences, while maintaining or even improving the text quality. In addition, we jointly train the models to predict the lengths, so our model can generate summaries with optimal length. We evaluate the proposed framework on the CNNDM dataset and show improved performance compared to existing methods.

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