MS2: Multi-Document Summarization of Medical Studies
This addresses the time-intensive and manual process of medical literature review for researchers, though it is incremental as it builds on existing summarization methods.
The authors tackled the problem of automating literature reviews for medical interventions by releasing MS2, a dataset of over 470k documents and 20k summaries, and achieved promising early results with a BART-based summarization system.
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 https://github.com/allenai/ms2