Change-Oriented Summarization of Temporal Scholarly Document Collections by Semantic Evolution Analysis

Abstract

The number of scholarly publications has dramatically increased over the last decades. For anyone new to a particular science domain it is not easy to understand the major trends and significant changes that the domain has undergone over time. Temporal summarization and related approaches should be then useful to make sense of scholarly temporal collections. In this paper we demonstrate an approach to analyze the dataset of research papers by providing a high level overview of important changes that occurred over time in this dataset. The novelty of our approach lies in the adaptation of methods used for semantic term evolution analysis. However, we analyze not just semantic evolution of single words independently, but we estimate common semantic drifts shared by groups of semantically converging words. As an example dataset we study the ACL Anthology Reference Corpus that spans from 1974 to 2015 and contains 22,878 scholarly articles.

Keywords

• Temporal summarization
• ACL
• clustering
• semantic changes
• cluster analysis