Summarization with Precise Length Control
Lesly Miculicich Yujia Xie Song Wang Pengcheng He
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.