학술논문

Change-Oriented Summarization of Temporal Scholarly Document Collections by Semantic Evolution Analysis
Document Type
Periodical
Source
IEEE Access Access, IEEE. 10:76401-76415 2022
Subject
Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Semantics
Task analysis
Context modeling
Machine learning
Linguistics
Analytical models
Syntactics
Temporal summarization
ACL
clustering
semantic changes
cluster analysis
Language
ISSN
2169-3536
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.