In this paper, we describe Topic Pages, an inventory of scientific concepts and information around them extracted from a large collection of scientific books and journals. The main aim of Topic Pages is to provide all the necessary information to the readers to understand scientific concepts they come across while reading scholarly content in any scientific domain. Topic Pages are a collection of automatically generated information pages using NLP and ML, each corresponding to a scientific concept. Each page contains three pieces of information: a definition, related concepts, and the most relevant snippets, all extracted from scientific peer-reviewed publications. In this paper, we discuss the details of different components to extract each of these elements. The collection of pages in production contains over 360,000 Topic Pages across 20 different scientific domains with an average of 23 million unique visits per month, constituting it a popular source for scientific information.

本文介绍了主题页面，这是从大量的科学书籍和期刊中提取的科学概念及其相关信息的库存。主题页面旨在为读者提供所有必要的信息，以便在任何科学领域阅读学术内容时理解科学概念。这篇文章讨论了自然语言处理（NLP）和机器学习（ML）不同组成部分的细节。其中，每个页面包含三个信息：定义，相关概念和最相关的摘录，均从科学同行评审出版物中提取而来。Topic Pages涵盖20个不同的科学领域，其在生产环境中约有360,000多个主题页面，每月平均23百万独立访问用户，是科学信息查询的一个热门来源。

使用科学出版物生成科学概念的主题页面