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Collections of research article data harvested from the web have becomecommon recently since they are important resources for experimenting on taskssuch as named entity recognition, text summarization, or keyword generation. Infact, certain types of experiments require collections that are both large andtopically structured, with records assigned to separate research disciplines.Unfortunately, the current collections of publicly available research articlesare either small or heterogeneous and unstructured. In this work, we performtopic segmentation of a paper data collection that we crawled and produce amultitopic dataset of roughly seven million paper data records. We construct ataxonomy of topics extracted from the data records and then annotate eachdocument with its corresponding topic from that taxonomy. As a result, it ispossible to use this newly proposed dataset in two modalities: as aheterogeneous collection of documents from various disciplines or as a set ofhomogeneous collections, each from a single research topic.

Topic Segmentation of Research Article Collections