Collections of research article data harvested from the web have become
common recently since they are important resources for experimenting on tasks
such as named entity recognition, text summarization, or keyword generation. In
fact, certain types of experiments require collections that are both large and
topically structured, with records assigned to separate research disciplines.
Unfortunately, the current collections of publicly available research articles
are either small or heterogeneous and unstructured. In this work, we perform
topic segmentation of a paper data collection that we crawled and produce a
multitopic dataset of roughly seven million paper data records. We construct a
taxonomy of topics extracted from the data records and then annotate each
document with its corresponding topic from that taxonomy. As a result, it is
possible to use this newly proposed dataset in two modalities: as a
heterogeneous collection of documents from various disciplines or as a set of
homogeneous collections, each from a single research topic.

该论文通过对网络上论文数据的主题分割，构建出一个包含七百万篇文献数据的多主题数据集，并创建出一套主题分类体系以注释每篇论文的主题归属，从而可以将其作为既包含各学科领域文献数据的异构文献资源集，又可分割为单一研究领域所对应的同质文献集的多主题数据集。