Topic Segmentation of Research Article Collections

TL;DR

This paper introduces a large, structured dataset of approximately seven million research articles, segmented by topics, to facilitate research tasks requiring topically organized collections.

Contribution

It presents a novel, large-scale, multitopic dataset of research articles with a constructed taxonomy and topic annotations, enabling diverse experimental applications.

Findings

Created a dataset of ~7 million research articles with topic annotations

Developed a taxonomy of research topics from the dataset

Enabled use of the dataset as heterogeneous or homogeneous collections

Abstract

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.