The scientific publication output grows exponentially. Therefore, it is
increasingly challenging to keep track of trends and changes. Understanding
scientific documents is an important step in downstream tasks such as knowledge
graph building, text mining, and discipline classification. In this workshop,
we provide a better understanding of keyword and keyphrase extraction from the
abstract of scientific publications.

本文介绍了如何从科学出版物的摘要中提取关键词和关键短语，以便于下游任务，如知识图谱建设、文本挖掘和学科分类。

科技文献中的关键词提取

Keyword Extraction in Scientific Documents

Peer review is the foundation of scientific publication, and the task of
reviewing has long been seen as a cornerstone of professional service. However,
the massive growth in the field of machine learning has put this community
benefit under stress, threatening both the sustainability of an effective
review process and the overall progress of the field. In this position paper,
we argue that a tragedy of the commons outcome may be avoided by emphasizing
the professional aspects of this service. In particular, we propose a rubric to
hold reviewers to an objective standard for review quality. In turn, we also
propose that reviewers be given appropriate incentive. As one possible such
incentive, we explore the idea of financial compensation on a per-review basis.
We suggest reasonable funding models and thoughts on long term effects.

介绍科学出版物的同行评审制度，讨论机器学习对此的影响，提出对标准和质量的强调以及提供适当的激励措施，如按照每次评审给予的财务补偿。