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Given the magnitude of the literature, a researcher must be selective of research papers and publications in general. In other words, only papers that meet strict standards of academic integrity and adhere to reliable and credible sources should be referenced. The purpose of this paper is to approach this issue from the prism of scientometrics according to the following research questions: Is it necessary to judge the quality of scientific production? How do we evaluate scientific production? What are the tools to be used in evaluation?

This paper presents a comparative study of scientometric evaluation practices and tools. A systematic literature review is conducted based on articles published in the field of scientometrics between 1951 and 2022. To analyze data, the authors performed three different aspects of analysis: usage analysis based on classification and comparison between the different scientific evaluation practices, type and level analysis based on classifying different scientometric indicators according to their types and application levels and similarity analysis based on studying the correlation between different quantitative metrics to identify similarity between them.

This comparative study leads to classify different scientific evaluation practices into externalist and internalist approaches. The authors categorized the different quantitative metrics according to their types (impact, production and composite indicators), their levels of application (micro, meso and macro) and their use (internalist and externalist). Moreover, the similarity analysis has revealed a high correlation between several scientometric indicators such as author h-index, author publications, citations and journal citations.

The interest in this study lies deeply in identifying the strengths and weaknesses of research groups and guides their actions. This evaluation contributes to the advancement of scientific research and to the motivation of researchers. Moreover, this paper can be applied as a complete in-depth guide to help new researchers select appropriate measurements to evaluate scientific production. The selection of evaluation measures is made according to their types, usage and levels of application. Furthermore, our analysis shows the similarity between the different indicators which can limit the overuse of similar measures.

Topic segmentation is one of the active research fields in natural language processing. Also, many topic segmenters have been proposed. However, the current challenge of researchers is the improvement of these segmenters by using external resources. Therefore, the purpose of this paper is to integrate study and evaluate a new external semantic resource in topic segmentation.

New topic segmenters (TSS-Onto and TSB-Onto) are proposed based on the two well-known segmenters C99 and TextTiling. The proposed segmenters integrate semantic knowledge to the segmentation process by using a domain ontology as an external resource. Subsequently, an evaluation is made to study the effect of this resource on the quality of topic segmentation along with a comparative study with related works.

Based on this study, the authors showed that adding semantic knowledge, which is extracted from a domain ontology, improves the quality of topic segmentation. Moreover, TSS-Ont outperforms TSB-Ont in terms of quality of topic segmentation.

The main limitation of this study is the used test corpus for the evaluation which is not a benchmark. However, we used a collection of scientific papers from well-known digital libraries (ArXiv and ACM).

The proposed topic segmenters can be useful in different NLP applications such as information retrieval and text summarizing.

The primary original contribution of this paper is the improvement of topic segmentation based on semantic knowledge. This knowledge is extracted from an ontological external resource.

This paper aims to propose a new qualitative indicator for the evaluation of the productions of researchers in any discipline.

Based on the study of existing quantitative indicators, the authors’ approach consisted of the hybridization of two indicators. This hybridization is based on the individual H_index (Hi_index) and H_index contemporary (Hc_index) weighted by qualitative factors. The initial sources of the data are online bibliographic databases, such as Google Scholar and Publish or Perish.

A new scientometric indicator was used to compare the scientific production quality of researchers and their classification (as part of a research community) as the classification of national and international research institutions. The authors have applied a new indicator to compare and classify the members of their laboratory, RIADI, according to their quality of scientific production.

The indicator is an improvement of the H_index. It is a measure that can have an impact on society (influencing research attitudes, affecting quality of research). By this contribution, the authors measure more than one aspect by involving all the external factors that can affect the quality of research.

This paper fulfils a gap in the literature concerning the absence of a qualitative indicator among the set of existing quantitative measures. Additionally, this paper addresses the limitations of the existing qualitative practices, such as peer review and citation analysis. In the new qualitative indicator, the authors involve all of these qualitative aspects: the influence of the age of the paper, the number of co-authors, the order of the co-authors, the impact factor of journals and the conference rankings.