  Controllable summarization allows users to generate customized summaries with
specified attributes. However, due to the lack of designated annotations of
controlled summaries, existing works have to craft pseudo datasets by adapting
generic summarization benchmarks. Furthermore, most research focuses on
controlling single attributes individually (e.g., a short summary or a highly
abstractive summary) rather than controlling a mix of attributes together
(e.g., a short and highly abstractive summary). In this paper, we propose
MACSum, the first human-annotated summarization dataset for controlling mixed
attributes. It contains source texts from two domains, news articles and
dialogues, with human-annotated summaries controlled by five designed
attributes (Length, Extractiveness, Specificity, Topic, and Speaker). We
propose two simple and effective parameter-efficient approaches for the new
task of mixed controllable summarization based on hard prompt tuning and soft
prefix tuning. Results and analysis demonstrate that hard prompt models yield
the best performance on all metrics and human evaluations. However,
mixed-attribute control is still challenging for summarization tasks. Our
dataset and code are available at https://github.com/psunlpgroup/MACSum.

Computation and Language