  Recent advances in summarization provide models that can generate summaries
of higher quality. Such models now exist for a number of summarization tasks,
including query-based summarization, dialogue summarization, and multi-document
summarization. While such models and tasks are rapidly growing in the research
field, it has also become challenging for non-experts to keep track of them. To
make summarization methods more accessible to a wider audience, we develop
SummerTime by rethinking the summarization task from the perspective of an NLP
non-expert. SummerTime is a complete toolkit for text summarization, including
various models, datasets and evaluation metrics, for a full spectrum of
summarization-related tasks. SummerTime integrates with libraries designed for
NLP researchers, and enables users with easy-to-use APIs. With SummerTime,
users can locate pipeline solutions and search for the best model with their
own data, and visualize the differences, all with a few lines of code. We also
provide explanations for models and evaluation metrics to help users understand
the model behaviors and select models that best suit their needs. Our library,
along with a notebook demo, is available at
https://github.com/Yale-LILY/SummerTime.
