To assess the effectiveness of any medical intervention, researchers must
conduct a time-intensive and highly manual literature review. NLP systems can
help to automate or assist in parts of this expensive process. In support of
this goal, we release MS^2 (Multi-Document Summarization of Medical Studies), a
dataset of over 470k documents and 20k summaries derived from the scientific
literature. This dataset facilitates the development of systems that can assess
and aggregate contradictory evidence across multiple studies, and is the first
large-scale, publicly available multi-document summarization dataset in the
biomedical domain. We experiment with a summarization system based on BART,
with promising early results. We formulate our summarization inputs and targets
in both free text and structured forms and modify a recently proposed metric to
assess the quality of our system's generated summaries. Data and models are
available at this https URL

本研究针对医学干预方案的评估，提供了一个达到自动评估医学文献、整合多个研究结果的新数据集：MS^2。并基于 BART 模型，通过自由文本和结构化形式，提出了用于评估自动生成摘要质量的新指标。