  Prompts with different control signals (e.g., length, keywords, etc.) can be
used to control text summarization. When control signals are available, they
can control the properties of generated summaries and potentially improve
summarization quality (since more information are given). Unfortunately,
control signals are not already available during inference time. In this paper,
we propose Lotus (shorthand for Latent Prompt Tuning for Summarization), which
is a single model that can be applied in both controlled and uncontrolled
(without control signals) modes. During training, Lotus learns latent prompt
representations from prompts with gold control signals using a contrastive
learning objective. Experiments show Lotus in uncontrolled mode consistently
improves upon strong (uncontrollable) summarization models across four
different summarization datasets. We also demonstrate generated summaries can
be controlled using prompts with user specified control tokens.
