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MS2: Multi-Document Summarization of Medical Studies

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arxiv 2104.06486 v3 pith:5YV6XXMN submitted 2021-04-13 cs.CL cs.AIcs.LG

Jay DeYoung , Iz Beltagy , Madeleine van Zuylen , Bailey Kuehl , Lucy Lu Wang This is my paper
classification cs.CLcs.AIcs.LG
keywords summarizationassessdatasetmedicalmulti-documentstudiesavailableliterature
verification ladder T0 review T1 audit T2 compute T3 formal
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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

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