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.

The rise of social media and the explosion of digital news in the web sphere have created new challenges to extract knowledge and make sense of published information. Automated timeline generation appears in this context as a promising answer to help users dealing with this information overload problem. Formally, Timeline Summarization (TLS) can be defined as a subtask of Multi-Document Summarization (MDS) conceived to highlight the most important information during the development of a story over time by summarizing long-lasting events in a timely ordered fashion. As opposed to traditional MDS, TLS has a limited number of publicly available datasets. In this paper, we propose TLS-Covid19 dataset, a novel corpus for the Portuguese and English languages. Our aim is to provide a new, larger and multi-lingual TLS annotated dataset that could foster timeline summarization evaluation research and, at the same time, enable the study of news coverage about the COVID-19 pandemic. TLS-Covid19 consists of 178 curated topics related to the COVID-19 outbreak, with associated news articles covering almost the entire year of 2020 and their respective reference timelines as gold-standard. As a final outcome, we conduct an experimental study on the proposed dataset over two extreme baseline methods. All the resources are publicly available at https://github.com/LIAAD/tls-covid19.

Abstract. This paper presents a research made with former Computer Science students of the Universidade Federal de Santa Maria in which the professional profile of these students is identified. In order to have a better view of the course and to set goals towards making it better, the research interviewed students that have finished their graduation between the years of 1996 and 2015. The research presents a confidence factor of 90% and sample error of 5%. The results obtained shows a great bias towards academia as well as a wide range of professional profiles of the students.Resumo. Este artigo apresenta uma análise acerca de um estudo realizado com alunos egressos do curso de Ciência da Computação da Universidade Federal de Santa Maria, a de modo a traçar um perfil do profissional graduado no curso por esta instituição. A fim de se ampliar perspectiva interna do curso e traçar metas capazes melhorar a qualidade do mesmo, tal levantamento deu-se por meio de uma pesquisa realizada com alunos formados no período de 1996à 2015. Para tanto, a pesquisa elaborada apresenta um nível de confiança de 80% com erro amostral de 6,5%. Os resultados obtidos demonstram uma grande tendência aárea acadêmica, bem como refletem o dinamismo e os diferentes perfis de um egresso do curso de Ciência da Computação.