LRV: A Tool for Academic Text Visualization to Support the Literature Review Process (original) (raw)
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Literature search and recommendation systems have traditionally focused on improving recommendation accuracy through new algorithmic approaches. Less research has focused on the crucial task of visualizing the retrieved results to the user. Today, the most common visualization for literature search and recommendation systems remains the ranked list. However, this format exhibits several shortcomings, especially for academic literature. We present an alternative visual interface for exploring the results of an academic literature retrieval system using a force-directed graph layout. The interactive information visualization techniques we describe allow for a higher resolution search and discovery space tailored to the unique feature-based similarity present among academic literature. RecVis - the visual interface we propose - supports academics in exploring the scientific literature beyond textual similarity alone, since it enables the rapid identification of other forms of similarit...
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Reviewing literatures for a certain research field is always important for academics. One could use Google-like information seeking tools, but oftentimes he/she would end up obtaining too many possibly related papers, as well as the papers in the associated citation network. During such a process, a user may easily get lost after following a few links for searching or cross-referencing. It is also difficult for the user to identify relevant/important papers from the resulting huge collection of papers. Our work, called PaperVis, endeavors to provide a user-friendly interface to help users quickly grasp the intrinsic complex citation-reference structures among a specific group of papers. We modify the existing Radial Space Filling (RSF) and Bullseye View techniques to arrange involved papers as a node-link graph that better depicts the relationships among them while saving the screen space at the same time. PaperVis applies visual cues to present node attributes and their transitions among interactions, and it categorizes papers into semantically meaningful hierarchies to facilitate ensuing literature exploration. We conduct experiments on the InfoVis 2004 Contest Dataset to demonstrate the effectiveness of PaperVis.
Visual Text Mining: Ensuring the Presence of Relevant Studies in Systematic Literature Reviews
International Journal of Software Engineering and Knowledge Engineering, 2015
One of the activities associated with the Systematic Literature Review (SLR) process is the selection review of primary studies. When the researcher faces large volumes of primary studies to be analyzed, the process used to select studies can be arduous. In a previous experiment, we conducted a pilot test to compare the performance and accuracy of PhD students in conducting the selection review activity manually and using Visual Text Mining (VTM) techniques. The goal of this paper is to describe a replication study involving PhD and Master students. The replication study uses the same experimental design and materials of the original experiment. This study also aims to investigate whether the researcher's level of experience with conducting SLRs and research in general impacts the outcome of the primary study selection step of the SLR process. The replication results have confirmed the outcomes of the original experiment, i.e., VTM is promising and can improve the performance of...
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2011
Background: A systematic literature review (SLR) is a methodology used to aggregate all relevant existing evidence to answer a research question of interest. Although crucial, the process used to select primary studies can be arduous, time consuming, and must often be conducted manually. Objective: We propose a novel approach, known as 'Systematic Literature Review based on Visual Text Mining' or simply SLR-VTM, to support the primary study selection activity using visual text mining (VTM) techniques. Method: We conducted a case study to compare the performance and effectiveness of four doctoral students in selecting primary studies manually and using the SLR-VTM approach. To enable the comparison, we also developed a VTM tool that implemented our approach. We hypothesized that students using SLR-VTM would present improved selection performance and effectiveness. Results: Our results show that incorporating VTM in the SLR study selection activity reduced the time spent in this activity and also increased the number of studies correctly included. Conclusions: Our pilot case study presents promising results suggesting that the use of VTM may indeed be beneficial during the study selection activity when performing an SLR.
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Action Science Explorer (ASE) is a tool designed to support users in rapidly generating readily consumable summaries of academic literature. It uses citation network visualization, ranking and filtering papers by network statistics, and automatic clustering and summarization techniques. We describe how early formative evaluations of ASE led to a mature system evaluation, consisting of an in-depth empirical evaluation with four domain experts. The evaluation tasks were of two types: predefined tasks to test system performance in common scenarios, and user-defined tasks to test the system's usefulness for custom exploration goals. The primary contribution of this paper is a validation of the ASE design and recommendations to provide: easy-to-understand metrics for ranking and filtering documents, user control over which document sets to explore, and overviews of the document set in coordinated views along with detailson-demand of specific papers. We contribute a taxonomy of features for literature search and exploration tools and describe exploration goals identified by our participants.
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In 2023 the project "(Ir)reproducibility of Scientific Research in the Digital Humanities?" started. The goal of the project is to promote reproducibility of methods used in the digital humanities and to develop basic guidelines for this purpose. A basis for reproducibility is the ability to distinguish between procedures or sub-procedures. One can find measures or distances, also costs or similarities at different points of the textual analysis pipeline. The intention of the calculation, which donates the name to the procedures, is always similar: to make a computational distinction between series of numbers. The series of numbers, also called multidimensional data or sometimes multivariate data, can stand for different things.-What they stand for, must be specified during the formalization of the research subject. The number of different measures is very big and confusing. The team designed a visualization that tries to show the effect of the measure. Through this we aim to support the selection of measures for text analysis tasks. The visualization includes a short description, a reference to the original source (where possible), and an interpretation in terms of simple textual heuristics and their distinction. This is a tool for both technical and non-technical staff. The visualizations provide the missing arguments in the formalization process, which helps to justify the methods. In the presentation we will introduce the online visualization and its usage. We will also talk about the implementation, the basic assumptions and the availability of the software on github.
README: A Literature Survey Assistant
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Literature review is an integral element of academic research, enabling researchers to learn about and build on existing work. Traditionally, this involves manually going through various published articles, either through following the citations in a reference paper, or via keywords on sites like Google Scholar. This process can often be tedious, and there is a high likelihood of missing out on certain related literature, owing to the sheer volume of publications every year. In addition, analyzing the advances and progression in a field requires a holistic view, which manual iteration over papers lacks. To this end, we propose README, an interactive tool aimed to aide with literature reviews. README would not only enables users to obtain a holistic view of various papers and topics, but also identifies and recommends relevant papers, given a reference paper the user is interested in. Additionally, it allows for chronological sorting of applicable papers, thus making analysis of tren...