Assessing the Quality of Scientific Papers
Roman Vainshtein, Gilad Katz, Bracha Shapira, Lior Rokach

TL;DR
This paper introduces a novel corpus linguistics-based method for assessing the overall quality of scientific papers within a specific field, demonstrated in computer science, and shows it effectively distinguishes high-impact papers from low-impact ones.
Contribution
The paper presents a new domain-specific quality measure and an associated classification method for scientific papers, validated in the computer science domain.
Findings
Significant score differences between high and low impact corpora
Proposed measure outperforms baseline classifier
Method applicable for automated scientific paper assessment
Abstract
A multitude of factors are responsible for the overall quality of scientific papers, including readability, linguistic quality, fluency,semantic complexity, and of course domain-specific technical factors. These factors vary from one field of study to another. In this paper, we propose a measure and method for assessing the overall quality of the scientific papers in a particular field of study. We evaluate our method in the computer science domain, but it can be applied to other technical and scientific fields.Our method is based on the corpus linguistics technique. This technique enables the extraction of required information and knowledge associated with a specific domain. For this purpose, we have created a large corpus, consisting of papers from very high impact conferences. First, we analyze this corpus in order to extract rich domain-specific terminology and knowledge. Then we…
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Taxonomy
TopicsText Readability and Simplification · Natural Language Processing Techniques · Topic Modeling
