Ricardo Campos Univ. of Beira Interior & INESCTEC & Ci2@IPT Portugal ricardo.campos@ubi.pt Alípio M. Jorge Univ. of Porto & INESCTEC Portugal amjorge@fc.up.pt Sumit Bhatia Adam Jatowt Univ. of Innsbruck Austria jatowt@acm.org Media and Data Science Research Lab, Adobe India Sumit.Bhatia@adobe.com Marina Litvak Shamoon College of Engineering Israel litvak.marina@gmail.com João Paulo Cordeiro Conceição Rocha Univ. of Beira Interior INESCTEC Portugal Portugal Hugo Sousa INESCTEC Portugal Behrooz  Mansouri University of Southern Maine USA
Abstract
The Seventh International Workshop on Narrative Extraction from Texts (Text2Story’24) was held on March 24 th , 2024, in conjunction with the 46 th European Conference on Infor- mation Retrieval (ECIR 2024) in Glasgow, Scotland. Over the day, more than 50 attendees engaged in discussions and presentations focused on recent advancements in narrative repre- sentation, extraction, and generation. The workshop featured two invited keynote addresses, fourteen research paper presentations, and a poster session. The workshop proceedings are available online. 1 Date: 24 March 2024. Website: https://text2story24.inesctec.pt/. ¹https://ceur-ws.org/Vol-3671/ 1
Introduction
1
The Text2Story workshop series was initiated to foster collaboration between researchers from These fields include In- various disciplines with a shared interest in narrative understanding. formation Retrieval, Natural Language Processing, Computational Linguistics, Artificial Intelli- gence, Human-Computer Interaction, and Data Visualization.  Building on the momentum from successful prior editions [Jorge et al., 2018, 2019a; Campos et al., 2020, 2021, 2022, 2023] and the Text2Story Special Issue at IPM Journal  [Jorge et al., 2019b], we had this year, the seventh iteration of the Text2Story workshop event at ECIR 2024. Figure 1 captures the organizers and the best paper award recipient. Figure 1. Organizers and the best paper award recipient at the Text2Story 2024 Workshop. This report provides an overview of the main activities that took place during the workshop. The attendees' enthusiastic engagement ensured that it was just as intellectually stimulating as previous editions.  A total of twenty-six submissions were received and each paper was reviewed by three PC members. The review process resulted in a total of fourteen papers being accepted for the workshop, providing a platform for researchers to share ongoing work and disseminate previously published works from other venues. The final program also featured two invited keynote talks and a vibrant and intellectually stimulating poster session. The presentation slides can be found here. 2
2
The Program
The workshop program featured two invited keynote addresses alongside fourteen research papers. These papers were presented both in oral sessions and during a dedicated poster session, fostering in-depth discussions among workshop participants. In the following section, we provide a concise overview of the research presented at t he workshop.
2.1
Keynotes
Professor Jochen L. Leidner [Leidner, 2024], from Coburg University of Applied Sciences and Arts, Germany, talked about the fundamental components of narratives and the core elements of stories. He focused on key questions about how to identify narrative signals within data, how to extract narratives from data, and how to present these narratives to users. He posited that humans are naturally inclined to understand and respond to narratives and stories rather than unprocessed facts. The second keynote by Mirella Lapata [Lapata, 2024], a professor at the School of Informatics at the University of Edinburgh, focused on controlled text generation to achieve a communicative goal, with a special emphasis on visual storytelling. She presented their framework that integrates visual representations with pre-trained language models and planning. This framework translates image sequences into a format interpretable by language models and utilizes question-answer pairs to guide narrative structure.  Evaluations demonstrate that this blueprint-based approach generates more coherent, interesting, and natural stories compared to the state-of-the-art methods.
2.2
Full Research Papers
The papers presented in the workshop spanned a wide variety of topics ranging from the use of Large Language Models (LLMs) for narrative understanding to the issues of identifying biased and harmful narratives. Matus et al. [2024] (with their paper entitled “ROGER: Extracting narratives using Large Language Models from Robert Gerstmann’s historical photo archive of the Sacambaya Expedition in 1928”) presented the ROGER framework for exploring and under- standing large historical image archives using large language models and AI-based image analysis. They also presented a case study illustrating their approach using archival images of the 1928 treasure hunt in Bolivia of The Sacambaya Exploration Company Limited. Silvano et al. [2024] (Untangling a web of temporal relations in news articles) aimed to analyze the temporal structure of news articles and how news articles express the order of events. The authors described a system to analyze the temporal relationships between events in news articles and create visualizations to understand how news stories unfold. This approach provides valuable insights into the structure of news narratives and can be used to improve models for understanding temporal information in text. Ocal et al. [2024] (Estimating Narrative Durations: Proof of Concept) considered the problem of estimating narrative duration and proposed an approach that combines timeline ex- traction techniques with event duration estimation methods. Further, the paper also introduced a large event duration dataset and noted challenges and future directions for improving the ap- proach. Macías and Norambuena [2024] (Evaluating the Ability of Computationally Extracted Narrative Maps to Encode Media Framing) explored narrative maps, a computational tech- nique, to extract framing information from news data. Framing refers to how news slants a story through  wording. The authors report that while narrative maps can capture the overall slant of the news data (framing distribution), the technique struggles to maintain consistent framing within individual stories, highlighting the need for future research in this direction. de Kok et al. [2024] (From Nodes to Narratives: A Knowledge Graph-based Story- telling Approach) presented a system to create complex narratives as event graphs, aiming for richer stories beyond the basic 4W elements (who, what, when, and where).  Although the authors did not observe clear improvements in quantitative metrics, the qualitative analysis indicated the importance of training on precise event relations for narrative generation, suggesting potential enhancements through further data augmentation and exploration of sub-event extraction tech- niques. Ezeani et al. [2024] (The Geography of ‘Fear’, ‘Sadness’, ‘Anger’ and ‘Joy’:  Exploring the Emotional Landscapes in the Holocaust Survivors’ Testimonies ) explored the idea of developing a computational framework to analyze emotional landscapes in textual narratives. They applied their proposed approach to ten Holocaust survivors' testimonies, focusing on four emotions: fear, sadness, anger, and joy, and presented their analysis. The authors plan to refine their methods, scale up the study, potentially fine-tune a model on Holocaust data, and explore zero-shot learning methodologies for future research. Gervás and López Calle [2024] (Representing Complex Relative Chronology Across Narrative Levels in Movie Plots) discussed the increasing complexity of movie narratives with non-linear chronologies and presented a model to represent these complexities. The authors explore challenges when narratives deviate from traditional chronology and suggest a representa- tion capturing embedded stories and future events within the narrative structure. Spillner [2024] (Unexpected Gender Stereotypes in AI-Generated Stories: Hair- dressers are Female, But so are Doctors) investigated the presence of gender bias within short stories generated by ChatGPT. The authors studied the gender assignments attributed to characters with specified professional roles within these stories and found a significant disparity in the perpetuation of gender stereotypes. While narratives featuring characters in traditionally female occupations strongly reinforce associated stereotypes, those depicting professions typically associated with men (e.g., doctors, scientists, attorneys, astronauts) demonstrate a tendency to- wards reversing such stereotypes, portraying these roles as potentially occupied by females. While identifying the bias in news articles and other sources of information is an important problem, research in this direction is often hindered by the unavailability of large-scale annotated datasets. In this paper, Raza et al. [2024] ( Dataset Annotation and Model Building for Identifying Biases in News Narratives) presented a pipeline for efficiently labeling data and developing models. The authors demonstrated the efficacy of the proposed pipeline by successfully annotating a dataset of 4,000 records and empirically evaluating different models for bias detection tasks. The annotated dataset and the best-performing model weights are made public for further research. Finally, Huang et al. [2024] (Event Extraction Alone Is Not Enough) presented Eventist, a new dataset for two important pairwise comparison tasks: event deduplication and event temporal ordering. The dataset consists of event instances related to storms, gathered from English news headlines across 90 countries. It covers 113 storm events that took place between January 1 st, 2021 and September 1 st , 2023, and includes a total of 23,304 news headlines.
2.3
Work-in-Progress, Position, Dissemination and Negative Result
Papers
The workshop also included special tracks for work-in-progress, position, dissemination, and nega- tive result papers to offer a platform for authors to share the initial results of their research efforts and gather useful feedback from the community, discuss a research vision or a reasoned opinion on the workshop topics, present summarized versions of previously published papers in high-quality conferences/journals, and highlight tested hypotheses that did not get the expected outcome. The following four papers were selected for these tracks. Gervás and Mendez [2024] with their negative result paper entitled ""Tagging Narrative with Propp’s Character Functions Using Large Language Models”, investigated the fea- sibility of using large language models (in a few-shot setting) to annotate stories with Vladimir Propp’s character functions by operating directly over the text of the stories. They found that the effectiveness of using the LLM for this task varies depending on the model’s familiarity with the character functions, suggesting the need for further exploration into different model variations and considerations of model size and sources. Kanjirangat et al. [2024] presented the preliminary results of a work-in-progress paper entitled "On the Limitations of Zero-Shot Classification of Causal Relations by LLMs, which explores large language models’ abilities to understand causal sentences in a zero-shot setting, comparing GPT and Falcon models with a fine-tuned BERT model. Despite the success of zero-shot approaches in various other tasks, they found that detecting causal relations is a challenging task for large language models. Reiter-Haas et al. [2024] introduced, in their position paper entitled "Computational Narrative Framing: Towards Identifying Frames through Contrasting the Evolution of Narrations", the task of com- putational narrative framing, integrating computational narrative understanding with framing analysis, highlighting shared future research directions crucial for narrative understanding. The authors used climate change discourse as an example to emphasize the importance of narrative structure, competing narratives, and temporal evolution for a thorough analysis. Finally, Hahamy et al. [2024] with their dissemination paper entitled "Integrating Cognitive Neuroscience In- sights into NLP: A New Approach to Understanding Narrative Processing” discussed how biological neural networks understand narratives, aiming to apply these insights to artificial neural networks.  Using functional Magnetic Resonance Imaging (fMRI), the study reveals that the human brain segments narratives into discrete events represented by neural activity, with events being bound to contextually relevant past events. The findings advocate for interdisciplinary col- laboration to incorporate cognitive principles into NLP models, potentially enhancing their ability to model and process of narratives.
3
Key Contributors
The Program Chairs were Ricardo Campos, Alípio Jorge, Adam Jatowt, Sumit Bhatia, and Marina Litvak.  The proceedings were setup by our Proceedings Chairs João Paulo Cordeiro and Conceição Rocha.  Hugo Sousa and Behrooz Mansouri were the web and dissemination chairs.  Ignatius Ezeani, Purificação Silvano, Satya Almasian, Alessandro Antonucci, and Hugo Sousa also chaired technical sessions at the workshop. Finally, we would like to acknowledge the effort and valuable contribution of the researchers and industry experts that have served on the Program Committee of the Text2Story’24 workshop. Our thanks go to: ● Álvaro Figueira (INESC TEC & University of Porto) ● Andreas Spitz (University of Konstanz) ● Antoine Doucet (Université de La Rochelle) ● António Horta Branco (University of Lisbon) ●  Anubhav Jangra (IIT Patna) ● Arian Pasquali (Faktion AI) ● Bart Gajderowicz (University of Toronto) ● Begoña Altuna (Universidad del País Vasco) ● Behrooz Mansouri(Rochester Institute of Technology) ● Brenda Santana (Federal University of Rio Grande do Sul) ● Bruno Martins (IST & INESC-ID, University of Lisbon) ● Daniel Loureiro (Cardiff University) ● Dennis Aumiller (Heidelberg University) ● Dhruv Gupta (Norwegian University of Science and Technology) ● Dyaa Albakour (Signal UK) ● Evelin Amorim (INESC TEC) ● Henrique Lopes Cardoso (LIACC & University of Porto) ● Hugo Sousa (INESC TEC & University of Porto) ● Ismail Altingovde (Middle East Technical University) ● Irina Rabaev (Shamoon College of Engineering) ● Jiexin Wang (South China University of Technology, China) ● João Paulo Cordeiro (INESC TEC & University of Beira Interior) ● Kiran Bandeli (Walmart Inc.) ● Liana Ermakova (HCTI, Université de Bretagne Occidentale) ● Luca Cagliero (Politecnico di Torino) ● Ludovic Moncla (INSA Lyon) ● Luis Filipe Cunha (INESC TEC & University of Minho) ● Marc Finlayson (Florida International University) ● Marc Spaniol (Université de Caen Normandie) ● Moreno La Quatra (Politecnico di Torino) ● Natalia Vanetik (Shamoon College of Engineering) ● Nuno Guimarães (INESC TEC & University of Porto) ● Pablo Gamallo (University of Santiago de Compostela) ● Pablo Gervás (Universidad Complutense de Madrid) ● Paulo Quaresma (Universidade de Évora) ● Paul Rayson (Lancaster University) ● Purificação Silvano (CLUP & University of Porto) ● Raghav Jain (Indian Institute of Technology, Patna) ● Ross Purves (University of Zurich) ● Satya Almasian (Heidelberg University) ● Sérgio Nunes (INESC TEC & University of Porto) ● Simra Shahid (Adobe's Media and Data Science Research Lab) ● Sriharsh Bhyravajjula (University of Washington) ● Udo Kruschwitz (University of Regensburg) ● Valentina Bartalesi (ISTI-CNR, Italy) ● Veysel Kocaman (John Snow Labs & Leiden University) ● Wenzhi Cao (University of Wisconsin, USA) ● Yang Zhang (Southwestern University of Finance and Economics, China)
Recognition Awards
44
The workshop ended with the announcement of the recognition awards.  The Text2Story 2024 Best Paper Award went to the paper entitled Tagging Narrative with Propp’s Character Functions Using Large Language Models by Pablo Gervás and Gonzalo Mendez. In addition to this, the workshop chairs have recognized the following researchers as recipients of the 2024 reviewer award for their insightful and valuable reviews. ● Bruno martins ● Dhruv Gupta ● Deya Banisakher ● Pablo Gervás ● Sérgio Nunes ● Sriharsh Bhyravajjula
Concluding Thoughts
5
The Text2Story workshop series has entered its seventh iteration, having initially emerged with the objective of f fostering collaboration between researchers from diverse backgrounds and areas of expertise to collectively define the research landscape for the nascent field of narrative extraction from text. The series has been met with significant enthusiasm within the research community, as evidenced by the steadily increasing number of workshop participants.
Acknowledgements
The success of this workshop would not have been possible without the invaluable support of the ECIR, 2024 organizers. We also extend our gratitude to the esteemed keynote speakers, program 77 committee members, paper authors, and participants whose valuable contributions significantly enriched the workshop experience.