Abstract versus Full Paper: A quantitative approach

Abstract The purpose of this article is to observe the differences between the words from selected papers and the terms from summaries or introductory chapters, that correspond with the selected papers, using quantitative methods. To test the above hypothesis, we took the terms from abstracts and introductory chapters, of three different type of papers (a scientific article, a literature book and a methodological study), and we compared them with the most frequent words from the selected papers. Furthermore, we also applied different methods from text mining, such as calculating the phi coefficient or checking compliance with a specific empirical law (Zipf, Heaps), to spot the existent dissimilarities. Following this analysis, it can be observed, from a quantitative point of view, that certain frequent words from the complete writings are found in their introductory components, present a fairly high frequency and outline the main ideas from the respective texts. This paper was co-financed by The Bucharest University of Economic Studies during the PhD program.

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Publication Details

Journal
Journal of Social and Economic Statistics
Published
2025-05-01
DOI
https://doi.org/10.2478/jses-2025-0003
Primary Topic
Biomedical Text Mining and Ontologies
Type
article
Field-Weighted Citation Impact
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article

Abstract versus Full Paper: A quantitative approach

Dimitri Calipolitis, Carla Pleştiu
Journal of Social and Economic Statistics
Biomedical Text Mining and Ontologies
article

Abstract versus Full Paper: A quantitative approach

Dimitri Calipolitis, Carla Pleştiu
article en

Abstract

Abstract The purpose of this article is to observe the differences between the words from selected papers and the terms from summaries or introductory chapters, that correspond with the selected papers, using quantitative methods. To test the above hypothesis, we took the terms from abstracts and introductory chapters, of three different type of papers (a scientific article, a literature book and a methodological study), and we compared them with the most frequent words from the selected papers. Furthermore, we also applied different methods from text mining, such as calculating the phi coefficient or checking compliance with a specific empirical law (Zipf, Heaps), to spot the existent dissimilarities. Following this analysis, it can be observed, from a quantitative point of view, that certain frequent words from the complete writings are found in their introductory components, present a fairly high frequency and outline the main ideas from the respective texts. This paper was co-financed by The Bucharest University of Economic Studies during the PhD program.

Journal of Social and Economic StatisticsVol. 14(1)
Bucharest University of Economic Studies (RO)
Openalex Percentile: Top 89%
Biomedical Text Mining and Ontologies
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Abstract versus Full Paper: A quantitative approach — Dimitri Calipolitis, Carla Pleştiu · Journal of Social and Economic Statistics (2025) | TGRS Research Map | TGRS