A Survey on Event-based News Narrative Extraction

Narratives are fundamental to our understanding of the world, providing us with a natural structure for knowledge representation over time. Computational narrative extraction is a subfield of artificial intelligence that makes heavy use of information retrieval and natural language processing techniques. Despite the importance of computational narrative extraction, relatively little scholarly work exists on synthesizing previous research and strategizing future research in the area. In particular, this article focuses on extracting news narratives from an event-centric perspective. Extracting narratives from news data has multiple applications in understanding the evolving information landscape. This survey presents an extensive study of research in the area of event-based news narrative extraction. In particular, we screened over 900 articles that yielded 54 relevant articles. These articles are synthesized and organized by representation model, extraction criteria, and evaluation approaches. Based on the reviewed studies, we identify recent trends, open challenges, and potential research lines.Comment: 37 pages, 3 figures, to be published in the journal ACM CSU

Summarize Dates First: A Paradigm Shift in Timeline Summarization

Timeline summarization aims at presenting long news stories in a compact manner. State-of-the-art approaches first select the most relevant dates from the original event timeline then produce per-date news summaries. Date selection is driven by either per-date news content or date-level references. When coping with complex event data, characterized by inherent news flow redundancy, this pipeline may encounter relevant issues in both date selection and summarization due to a limited use of news content in date selection and no use of high-level temporal references (e.g., the past month). This paper proposes a paradigm shift in timeline summarization aimed at overcoming the above issues. It presents a new approach, namely Summarize Date First, which focuses on first generating date-level summaries then selecting the most relevant dates on top of summarized knowledge. In the latter stage, it performs date aggregations to consider high-level temporal references as well. The proposed pipeline also supports frequent incremental timeline updates more efficiently than previous approaches. We tested our unsupervised approach both on existing benchmark datasets and on a newly proposed benchmark dataset describing the COVID-19 news timeline. The achieved results were superior to state-of-the-art unsupervised methods and competitive against supervised ones

Proceedings of the First Workshop on Computing News Storylines (CNewsStory 2015)

This volume contains the proceedings of the 1st Workshop on Computing News Storylines (CNewsStory 2015) held in conjunction with the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2015) at the China National Convention Center in Beijing, on July 31st 2015. Narratives are at the heart of information sharing. Ever since people began to share their experiences, they have connected them to form narratives. The study od storytelling and the field of literary theory called narratology have developed complex frameworks and models related to various aspects of narrative such as plots structures, narrative embeddings, characters’ perspectives, reader response, point of view, narrative voice, narrative goals, and many others. These notions from narratology have been applied mainly in Artificial Intelligence and to model formal semantic approaches to narratives (e.g. Plot Units developed by Lehnert (1981)). In recent years, computational narratology has qualified as an autonomous field of study and research. Narrative has been the focus of a number of workshops and conferences (AAAI Symposia, Interactive Storytelling Conference (ICIDS), Computational Models of Narrative). Furthermore, reference annotation schemes for narratives have been proposed (NarrativeML by Mani (2013)). The workshop aimed at bringing together researchers from different communities working on representing and extracting narrative structures in news, a text genre which is highly used in NLP but which has received little attention with respect to narrative structure, representation and analysis. Currently, advances in NLP technology have made it feasible to look beyond scenario-driven, atomic extraction of events from single documents and work towards extracting story structures from multiple documents, while these documents are published over time as news streams. Policy makers, NGOs, information specialists (such as journalists and librarians) and others are increasingly in need of tools that support them in finding salient stories in large amounts of information to more effectively implement policies, monitor actions of “big players” in the society and check facts. Their tasks often revolve around reconstructing cases either with respect to specific entities (e.g. person or organizations) or events (e.g. hurricane Katrina). Storylines represent explanatory schemas that enable us to make better selections of relevant information but also projections to the future. They form a valuable potential for exploiting news data in an innovative way.JRC.G.2-Global security and crisis managemen

Characterizing documents about colombian indigenous peoples using text analytics

The indigenous peoples of Colombia have a considerable social, political and cultural wealth. However, issues such as the decadeslong armed conflict and drug trafficking have posed a significant threat to their survival. In this work, publically available documents on the Internet with information about two indigenous communities, the Aw´a and Inga people from the Cauca region in southern Colombia, are analyzed using automated text analytics approaches. A corpus is constructed comprising general characterization documents, media articles and sentences from the Constitutional Court. Topic analysis is carried out to identify the relevant themes in the corpus to characterize each community. Sentiment analysis carried out on the media articles indicates that the articles about the Inga tend to be more positive and objective than the Aw´a. This may be attributed to the significant impact that the armed conflict has had on the Awa´ in recent years, and the productive projects of the Inga. Furthermore, an approach for summarizing long, complex documents by means of timelines is illustrated with a sentence issued by the Constitutional Court. It is concluded that such an approach has significant potential to facilitate understanding of documents of this nature

Assessing the feasibility of a life history calendar to measure HIV risk and health in older South Africans

Life history calendars capture patterns of behavior over time, uncovering transitions and trajectories. Despite the growing numbers of older persons living with HIV in southern Africa, little is known about how HIV testing and risk unfold in this population. Operationalizing a life course approach with the use of an innovative Testing and Risk History Calendar [TRHC], we collected pilot data on older South Africans’ risk and HIV testing. We found older persons were able to provide (1) reference points to facilitate recall over a 10-year period, (2) specifics about HIV tests during that decade, and (3) details that contextualize the testing data, such as living arrangements, relationships, and health status. Interviewer debriefing sessions after each interview captured information on context and links across domains. On a larger scale, the TRHC has potential to reveal pathways between sexual behavior, HIV testing and risk perception, and health at older ages

Content Selection for Timeline Generation from Single History Articles

This thesis investigates the problem of content selection for timeline generation from single history articles. While the task of timeline generation has been addressed before, most previous approaches assume the existence of a large corpus of history articles from the same era. They exploit the fact that salient information is likely to be mentioned multiple times in such corpora. However, large resources of this kind are only available for historical events that happened in the most recent decades. In this thesis, I present approaches which can be used to create history timelines for any historical period, even for eras such as the Middle Ages, for which no large corpora of supplementary text exist. The thesis first presents a system that selects relevant historical figures in a given article, a task which is substantially easier than full timeline generation. I show that a supervised approach which uses linguistic, structural and semantic features outperforms a competitive baseline on this task. Based on the observations made in this initial study, I then develop approaches for timeline generation. I find that an unsupervised approach that takes into account the article's subject area outperforms several supervised and unsupervised baselines. A main focus of this thesis is the development of evaluation methodologies and resources, as no suitable corpora existed when work began. For the initial experiment on important historical figures, I construct a corpus of existing timelines and textual articles, and devise a method for evaluating algorithms based on this resource. For timeline generation, I present a comprehensive evaluation methodology which is based on the interpretation of the task as a special form of single-document summarisation. This methodology scores algorithms based on meaning units rather than surface similarity. Unlike previous semantic-units-based evaluation methods for summarisation, my evaluation method does not require any manual annotation of system timelines. Once an evaluation resource has been created, which involves only annotation of the input texts, new timeline generation algorithms can be tested at no cost. This crucial advantage should make my new evaluation methodology attractive for the evaluation of general single-document summaries beyond timelines. I also present an evaluation resource which is based on this methodology. It was constructed using gold-standard timelines elicited from 30 human timeline writers, and has been made publicly available. This thesis concentrates on the content selection stage of timeline generation, and leaves the surface realisation step for future work. However, my evaluation methodology is designed in such a way that it can in principle also quantify the degree to which surface realisation is successful

Working with a young people’s advisory panel to conduct educational research:Young people’s perspectives and researcher reflections

Participatory Action Research (PAR) with young people aims to centre their knowledge and experience in research which is meaningful to them. In recent years, there has been an increase in PAR approaches within education, yet there is still a need for greater methodological insight into this approach. In this project, which explored adolescents’ reading motivation and engagement, a young people's advisory panel was convened to ensure the perspectives and experiences of young people were central to the project. The panel consisted of 6 young people (13–15-years-old) from 3 geographically dispersed schools in Scotland. The panel worked with researchers at the Universities of Edinburgh and Dundee and a national literacy organisation across one academic year to plan and design the project, carry out data collection, and support interpretation the findings. In this article, young peoples’ perspectives on their role and adult perspectives on the methodological approach of working with a young people's advisory panel on a reading research project are explored. Discussion of the benefits (e.g., challenging systems of power and privilege, producing outcomes which are more relevant to pupils), limitations (e.g., truly disrupting hierarchies of power), and considerations (e.g., planning participatory projects, including diverse and representative voices, and ‘bounded empowerment’) for researchers interested in convening youth advisory panels for educational research are provided to contribute towards the growing interest in PAR approaches in educational research

Insights into How HIAs are Characterized in the Press: Findings from a Media Analysis of Widely Circulated United States Newspapers

Background: Health impact assessments (HIAs) are burgeoning tools in the policy process, where the media plays a critical role by focusing attention on issues, informing consumers, and influencing positions. Examining how media portrays HIAs is critical to understanding HIAs in the policy context. Methods: This study considered how widely circulated, U.S. newspapers represent HIAs. After searching newspaper databases, we used a qualitative document analysis method consisting of open and axial coding to examine specific phrases of HIA depictions. Results: In coding over 1,000 unique phrases from the 62 documents generated in our search, we found an uptick in HIA-related publications since 2010. Coding these documents identified 46 distinct codes across 10 different themes. The two most prominent HIA-centered themes focused on HIA engagement and the HIA setting. While themes of policy and science, health determinants, and explanations of HIAs were also frequently featured, specific mentions of projected impacts, HIA processes, HIA values, and health outcomes were less prevalent. Conclusions: HIA media portrayals warrant further inquiry from researchers and practitioners. Focusing on how media portrays HIAs is consistent with several HIA steps. It is also important for a broader strategy to educate stakeholders about HIAs and to understand HIAs’ utility. HIA practitioners should develop and implement guidelines for media interaction and tracking that encourage practitioners to seek additional media attention and to focus such attention on health impacts and outcomes, HIA recommendations, and HIA values. Building on our work, researchers should examine HIA media portrayals beyond the context of this study