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The 7th International Workshop on Narrative Extraction from Texts: Text2Story 2024

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Advances in Information Retrieval (ECIR 2024)

Abstract

The Text2Story Workshop series, dedicated to Narrative Extraction from Texts, has been running successfully since 2018. Over the past six years, significant progress, largely propelled by Transformers and Large Language Models, has advanced our understanding of natural language text. Nevertheless, the representation, analysis, generation, and comprehensive identification of the different elements that compose a narrative structure remains a challenging objective. In its seventh edition, the workshop strives to consolidate a common platform and a multidisciplinary community for discussing and addressing various issues related to narrative extraction tasks. In particular, we aim to bring to the forefront the challenges involved in understanding narrative structures and integrating their representation into established frameworks, as well as in modern architectures (e.g., transformers) and AI-powered language models (e.g., chatGPT) which are now common and form the backbone of almost every IR and NLP application. Text2Story encompasses sessions covering full research papers, work-in-progress, demos, resources, position and dissemination papers, along with keynote talks. Moreover, there is dedicated space for informal discussions on methods, challenges, and the future of research in this dynamic field.

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Acknowledgements

Ricardo Campos is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within the project StorySense (10.54499/2022.14409312.PTDC) and Alípio Jorge within project LA/P/0063/2020.

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Campos, R., Jorge, A., Jatowt, A., Bhatia, S., Litvak, M. (2024). The 7th International Workshop on Narrative Extraction from Texts: Text2Story 2024. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14612. Springer, Cham. https://doi.org/10.1007/978-3-031-56069-9_52

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  • DOI: https://doi.org/10.1007/978-3-031-56069-9_52

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