SummerTime: Text Summarization Toolkit for Non-experts

TL;DR

SummerTime is a comprehensive, user-friendly toolkit designed to make text summarization models and tasks accessible to non-experts, enabling easy model comparison, visualization, and understanding.

Contribution

It introduces a complete summarization toolkit with integrated models, datasets, evaluation metrics, and explanations tailored for NLP non-experts.

Findings

Provides an easy-to-use API for summarization tasks

Includes visualization tools for model comparison

Offers explanations to aid understanding of models and metrics

Abstract

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.