# Storytelling with Image Data: A Systematic Review and Comparative Analysis of Methods and Tools
## 2 Literature Selection  
### Background and Motivation  
This work is a systematic literature review (SLR) on storytelling with image data based on the standard guidelines for performing SLR in software engineering [12]. The most crucial aspect of any SLR is identifying the research questions (RQs). Table 1 demonstrates the list of research questions (RQs), the discussion, and the main motivation that we focus on to identify state-of-the-art storytelling methods with image data, which is discussed in detail in Section 2.2. Moreover, Figure 3a shows a word cloud representation of research questions and their corresponding main motivations, with more frequent words in larger font sizes. Word clouds, in general, are visual representations of textual data. They highlight the most prominent or frequent words and ignore the most common words in the language. These figures pinpoint the most important keywords in our search keywords and question-motivation pairs for storytelling with the image data field. Finally, the detailed classification of different categories with their subcategories, references, time period, and the number of references in each subcategory is mentioned in Table 2 in this survey. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**ID** & **Research Question** & **Description and Main Motivation** \\ \hline RQ1 & What is storytelling with image data, and how can it contribute to understanding images? & Storytelling with image data aims to generate a coherent story based on a set of relevant images. The main steps to understanding image data are curation, summarization, and presentation in a succinct and consumable manner to end-users. Storytelling with image data is therefore considered an appropriate metaphor for capturing and representing the temporal/sequential order of key events. \\ \hline RQ2 & How is storytelling with image data different from image/video captioning methods? & Storytelling with image data could differ from image/video captioning, classification, and labeling tasks. In storytelling, the objective is to describe a set of images subjectively. Due to different understandings, a data analyst may create various narratives for a set of images. Based on the combinations of these narratives and the goals of an analyst, personalized and customized stories could be generated. \\ \hline RQ3 & What are the differences between storytelling with a set of related images rather than a single image? & Storytelling with a single image could differ from storytelling with a set of images. Multiple images generate more features and consist of more dimensions, such as time and location, resulting in diverse knowledge and context-aware narratives. For example, imagine a single image showing a nervous woman in a meeting vs. multiple images depicting her activities, such as waking up, having breakfast with family, going to work, being late and stuck in traffic, being in a meeting nervously, and finally going back home. \\ \hline RQ4 & What are the technical challenges we face in storytelling with image data? & Storytelling mainly focuses on understanding a set of related images. There is a plethora of research in storytelling with textual data, which is well-matured, and there has been significant progress in this field. It can develop, focus, and deliver critical concepts from an unstructured corpus into a narrative. However, the field of storytelling with image data is at an early stage. The challenges in storytelling fundamentally differ from simple text generation based on an image. Filling in the visual gap between the images with a subjective story is the main challenge of storytelling with image data. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Research questions (RQs) examined in this survey.