Change Summarization of Diachronic Scholarly Paper Collections by Semantic Evolution Analysis

TL;DR

This paper introduces a novel semantic evolution analysis method for summarizing changes in scholarly paper collections over time, aiding researchers and historians in understanding scientific progress and trends.

Contribution

It presents a new approach to analyze semantic changes in research paper collections over time using word semantic representations, enhancing temporal summarization tools.

Findings

Effectively captures semantic shifts in scholarly collections from 1979 to 2015.

Provides high-level overviews of scientific domain evolution.

Applied to ACL Anthology with 22,878 papers.

Abstract

The amount of scholarly data has been increasing dramatically over the last years. For newcomers to a particular science domain (e.g., IR, physics, NLP) it is often difficult to spot larger trends and to position the latest research in the context of prior scientific achievements and breakthroughs. Similarly, researchers in the history of science are interested in tools that allow them to analyze and visualize changes in particular scientific domains. Temporal summarization and related methods should be then useful for making sense of large volumes of scientific discourse data aggregated over time. We demonstrate a novel approach to analyze the collections of research papers published over longer time periods to provide a high-level overview of important semantic changes that occurred over the progress of time. Our approach is based on comparing word semantic representations over time and aims to support users in a better understanding of large domain-focused archives of scholarly publications. As an example dataset we use the ACL Anthology Reference Corpus that spans from 1979 to 2015 and contains 22,878 scholarly articles.