P. Deligiannis, Thanasis Vergoulis, Serafeim Chatzopoulos, Christos Tryfonopoulos
{"title":"Visualising Scientific Topic Evolution","authors":"P. Deligiannis, Thanasis Vergoulis, Serafeim Chatzopoulos, Christos Tryfonopoulos","doi":"10.1145/3442442.3451371","DOIUrl":null,"url":null,"abstract":"The automatic extraction of topics is a standard technique for summarizing text corpora from various domains (e.g., news articles, transport or logistic reports, scientific publications) that has several applications. Since, in many cases, topics are subject to continuous change there is the need to monitor the evolution of a set of topics of interest, as the corresponding corpora are updated. The evolution of scientific topics, in particular, is of great interest for researchers, policy makers, fund managers, and other professionals/engineers in the research and academic community. In this work, we demonstrate a prototype that provides intuitive visualisations for the evolution of scientific topics providing insights about topic transformation, merging, and splitting during the recent years. Although the prototype works on top of a scientific text corpus, its implementation is generic and can be easily applied on texts from other domains, as well.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Companion Proceedings of the Web Conference 2021","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3442442.3451371","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The automatic extraction of topics is a standard technique for summarizing text corpora from various domains (e.g., news articles, transport or logistic reports, scientific publications) that has several applications. Since, in many cases, topics are subject to continuous change there is the need to monitor the evolution of a set of topics of interest, as the corresponding corpora are updated. The evolution of scientific topics, in particular, is of great interest for researchers, policy makers, fund managers, and other professionals/engineers in the research and academic community. In this work, we demonstrate a prototype that provides intuitive visualisations for the evolution of scientific topics providing insights about topic transformation, merging, and splitting during the recent years. Although the prototype works on top of a scientific text corpus, its implementation is generic and can be easily applied on texts from other domains, as well.