Abstract
News articles typically include reporting events to inform on what happened. These reporting events are not part of the story being told but are nonetheless a relevant part of the news and can pose a challenge to the computational processing of news narratives. They compose a reporting narrative, which is the present study's focus. This paper aims to demonstrate through selected use cases how a comprehensive annotation scheme with suitable tags and links can properly represent the reporting events and the way they relate to the events that make the story. In addition, we put forward a proposal for their visual representation that enables a systematic and detailed analysis of the importance of reporting events in the news structure. Finally, we describe some lexico-grammatical features of reporting events, which can contribute to their automatic detection. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Abstract
Visual representations have the potential to improve information understanding. We explore this idea in the development of NewsLine, an open-source web-based prototype that focuses on narrative visualizations of news content. Having structured data as input, the prototype produces a storyline which showcases the narrative's events and participants, allowing the user to interact with the visualization in a number of ways. We built an information hub around the storyline to allow for multiple levels of exploration, specifically the main visualization, the event information module, and the sidebar. The visualization depicts the sequence of events that make up a news story, as well as the interactions between the involved parties in each event. The event information module presents additional information on a particular event. The sidebar is the “control center” of the visualization, unlocking a number of interactions and configurations. The prototype was evaluated with a user study with journalists and also with an online survey which gathered feedback from 178 potential end users. From these, 106 participants (60.6%) provided a rating of four or above (one to five scale) when asked to quantify their interest in using the application. Moreover, participants were asked to rank the importance of the visualization elements used. The results highlight that two elements stand out as the most important, the events and the entities. Overall, the participants generally found the application to be useful, but in need of some work in order for it to be made available to a broader public. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Abstract
Narratives are present in many forms of human expression and can be understood as a fundamental way of communication between people. Computational understanding of the underlying story of a narrative, however, may be a rather complex task for both linguists and computational linguistics. Such task can be approached using natural language processing techniques to automatically extract narratives from texts. In this paper, we present an in depth survey of narrative extraction from text, providing a establishing a basis/framework for the study roadmap to the study of this area as a whole as a means to consolidate a view on this line of research. We aim to fulfill the current gap by identifying important research efforts at the crossroad between linguists and computer scientists. In particular, we highlight the importance and complexity of the annotation process, as a crucial step for the training stage. Next, we detail methods and approaches regarding the identification and extraction of narrative components, their linkage and understanding of likely inherent relationships, before detailing formal narrative representation structures as an intermediate step for visualization and data exploration purposes. We then move into the narrative evaluation task aspects, and conclude this survey by highlighting important open issues under the domain of narratives extraction from texts that are yet to be explored.

Abstract
Narratives are used to convey information and are an important way of understanding the world through information sharing. With the increasing development in Natural Language Processing and Artificial Intelligence, it becomes relevant to explore new techniques to extract, process, and visualize narratives. Narrative visualization tools enable a news story reader to have a different perspective from the traditional format, allowing it to be presented in a schematic way, using representative symbols to summarize it. We propose a new narrative visualization approach using icons to represent important narrative elements. The proposed visualization is integrated in Brat2Viz, a narrative annotation visualization tool that implements a pipeline that transforms text annotations into formal representations leading to narrative visualizations. To build the icon visualization, we present a narrative element extraction process that uses automatic sentence extraction, automatic translation methods, and an algorithm that determines the actors' most adequate descriptions. Then, we introduce a method to create an icon dictionary, with the ability to automatically search for icons. Furthermore, we present a critical analysis and user-based evaluation of the results resorting to the responses collected in two separate surveys.

Abstract
In this article, we present solutions to visualize and interact with linked data in historical archives considering three different scenarios: search, individual record view, and creation of relationships. The created solutions were designed using nonfunctional mockups and were based on the CIDOC-CRM model, a model created and applied in the museums community liable to be extended to other cultural heritage institutions, being our solutions an application of this model to archives. A sample of 20 archival professionals was selected to evaluate the elements included in the proposed solutions. The evaluation sessions consisted in structured interviews supported by an introductory video and a survey. The think-aloud protocol was applied during the sessions. We conducted both a quantitative and qualitative analysis to the collected answers. From this analysis, we conclude that the majority of the participants showed great receptivity to the solutions presented and recognized many benefits in the application of linked data. Our contributions also include an exploratory study of some existing linked data systems, giving particular attention to their visualization and interaction modes.