Topic Segmentation of Research Article Collections
This provides a resource for researchers needing structured data for tasks like named entity recognition or text summarization, though it is incremental as it builds on existing data collection methods.
The authors tackled the lack of large, topically structured research article collections by performing topic segmentation on a crawled dataset of roughly seven million records, creating a multitopic dataset that can be used as either heterogeneous or homogeneous collections.
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.