mup-full

# MuP - Multi Perspective Scientific Document Summarization

Generating summaries of scientific documents is known to be a challenging task. Majority of existing work in summarization assumes only one single best gold summary for each given document. Having only one gold summary negatively impacts our ability to evaluate the quality of summarization systems as writing summaries is a subjective activity. At the same time, annotating multiple gold summaries for scientific documents can be extremely expensive as it requires domain experts to read and understand long scientific documents. This shared task will enable exploring methods for generating multi-perspective summaries. We introduce a novel summarization corpus, leveraging data from scientific peer reviews to capture diverse perspectives from the reader's point of view.

For more information about the dataset please refer to: https://github.com/allenai/mup

# MuP——多视角科学文献摘要生成
科学文献摘要生成向来是一项极具挑战性的任务。现有摘要生成领域的绝大多数研究均假定，每份给定文献仅对应一份最优金标准摘要（gold summary）。由于摘要撰写本身属于主观行为，仅依赖单份金标准摘要会对摘要生成系统的质量评估工作造成负面影响。与此同时，为科学文献标注多份金标准摘要的成本极高，因为这需要领域专家阅读并理解篇幅较长的专业科学文献。本次共享任务旨在探索多视角摘要生成的相关方法。我们依托科学同行评审（peer review）数据构建了全新的摘要生成语料库，该语料库能够从读者视角捕捉多样化的摘要视角。
如需了解该数据集的更多详情，请访问：https://github.com/allenai/mup