Connectionists: Text2Story at ECIR'24 Call for Papers
Hugo Oliveira Sousa
hugo.o.sousa at inesctec.pt
Fri Dec 15 14:32:41 EST 2023
*** Apologies for cross-posting ***
++ CALL FOR PAPERS ++
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Seventh International Workshop on Narrative Extraction from Texts (Text2Story'24)
Held in conjunction with the 46th European Conference on Information Retrieval (ECIR'24)
March 24th, 2024 – Glasgow, Scotland
Website: https://text2story24.inesctec.pt<https://text2story24.inesctec.pt/>
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++ Important Dates ++
- Submission Deadline: January 10th, 2024
- Acceptance Notification: February 23rd, 2024
- Camera-ready copies: March 15th, 2024
- Workshop: March 24th, 2024
++ Overview ++
Over these past years, significant breakthroughs, led by Transformers and Large Language Models (LLMs), have been made in understanding natural language text. However, the ability to capture, represent, and analyze contextual nuances in longer texts is still an elusive goal, let alone the understanding of consistent fine-grained narrative structures in text. In the seventh edition of the Text2Story workshop, we aim to bring to the forefront the challenges involved in understanding the structure of narratives and in incorporating their representation in well-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. It is hoped that the workshop will provide a common forum to consolidate the multi-disciplinary efforts and foster discussions to identify the wide-ranging issues related to the narrative extraction task.
++ List of Topics ++
Research works submitted to the workshop should foster the scientific advance on all aspects of storyline generation and understanding from texts including but not limited to narrative information extraction aspects, narratives representation, knowledge extraction, ethics and bias in narratives, datasets and evaluation protocols and narrative applications such as visualization of narratives, multi-modal aspects, Q&A, etc. To this regard, we encourage the submission of high-quality and original submissions covering the following topics:
Information Extraction Aspects
*
Temporal Relation Identification
*
Temporal Reasoning and Ordering of Events
*
Causal Relation Extraction and Arrangement
*
Big Data Applied to Narrative Extraction
Narrative Representation
*
Annotation protocols
*
Narrative Representation Models
*
Lexical, Syntactic, and Semantic Ambiguity in Narrative Representation
Narrative Analysis and Generation
*
Argumentation Analysis
*
Language Models and Transfer Learning in Narrative Analysis
*
Narrative Analysis in Low-resource Languages
*
Multilinguality: Multilingual and Cross-lingual Narrative Analysis
*
Comprehension of Generated Narratives
*
Story Evolution and Shift Detection
*
Automatic Timeline Generation
Datasets and Evaluation Protocol
*
Evaluation Methodologies for Narrative Extraction
*
Annotated datasets
*
Narrative Resources
Ethics and Bias in Narratives
*
Bias Detection and Removal in Generated Stories
*
Ethical and Fair Narrative Generation
*
Misinformation and Fact Checking
Narrative Applications
*
Narrative-focused Search in Text Collections
*
Narrative Summarization
*
Narrative Q&A
*
Multi-modal Narrative Summarization
*
Sentiment and Opinion Detection in Narratives
*
Social Media Narratives
*
Narrative Simplification
*
Personalization and Recommendation of Narratives
*
Storyline Visualization
++ Dataset ++
We challenge the interested researchers to consider submitting a paper that makes use of the tls-covid19 dataset - published at ECIR'21 - under the scope and purposes of the text2story workshop. tls-covid19 consists of a number of curated topics related to the Covid-19 outbreak, with associated news articles from Portuguese and English news outlets and their respective reference timelines as gold-standard. While it was designed to support timeline summarization research tasks it can also be used for other tasks (e.g., Q&A), especially when combined with Large Language Models (LLMs) like ChatGPT. A script to reconstruct and expand the dataset is available at https://github.com/LIAAD/tls-covid19. The article itself is available at this link: https://link.springer.com/chapter/10.1007/978-3-030-72113-8_33
++ Submission Guidelines ++
We solicit the following types of contributions:
* Full papers
up to 8 pages + references
Original and high-quality unpublished contributions to the theory and practical aspects of the narrative extraction task. Full papers should introduce existing approaches, describe the methodology and the experiments conducted in detail. Negative result papers to highlight tested hypotheses that did not get the expected outcome are also welcomed.
* Short papers
up to 5 pages + references
Unpublished short papers describing work in progress; position papers introducing a new point of view, a research vision or a reasoned opinion on the workshop topics; and dissemination papers describing project ideas, ongoing research lines, case studies or summarized versions of previously published papers in high-quality conferences/journals that is worthwhile sharing with the Text2Story community, but where novelty is not a fundamental issue.
* Demos | Resource Papers
up to 5 pages + references
Unpublished papers presenting research/industrial demos; papers describing important resources (datasets or software packages) to the text2story community;
Submissions will be peer-reviewed by at least two members of the programme committee. The accepted papers will appear in the proceedings published at CEUR workshop proceedings (indexed in Scopus and DBLP) as long as they don't conflict with previous publication rights.
++ Workshop Format ++
Participants of accepted papers will be given 15 minutes for oral presentations.
++ Invited Speakers ++
Homo narrans: From Information to Narratives
Jochen L. Leidner<https://www.coburg-university.de/about-us/faculties/faculty-of-business-and-economics/prof-jochen-l-leidner.html>, Coburg University of Applied Sciences, Germany
Abstract: Humans are curious creatures, equipped with a sense of (and desire for) finding meaning in their environment. They are predisposed to identify patterns, real and spurious, in the world they live in, and above anything else, they understand the world in terms of narratives. In this talk, we will explore a set of questions about narratives: what is a narrative made up of? What signals from textual prose tell us what the narrative is? What about signals from structured data that imply a particular narrative? What is the essence of a story? How can narrative information be extracted and presented? Open source intelligence analysts and investigative reporters alike are hunting for the story, the narrative, behind the petabyte intercepts or terabyte leaks. The more data we gather or have available, the stronger will be our thirst to distill meaningful stories from it.
Bio: Professor Jochen L. Leidner MA MPhil PhD FRGS is the Research Professor for Explainable and Responsible Artificial Intelligence in Insurance at Coburg University of Applied Sciences and Arts, Germany, where he leads the Information Access Research Group, a Visiting Professor of Data Analytics in the Department of Computer Science, University of Sheffield and founder and CEO of the consultancy KnowledgeSpaces. He is also a Fellow of the Royal Geographical Society. Dr. Leidner's experience includes positions as Director of Research at Thomson Reuters and Refinitiv in London, where he headed its R&D team (2013-2022). He has built up research and innovation teams. He was also the Royal Academy of Engineering Visiting Professor of Data Analytics at the Department of Computer Science. His background includes a Master's in computational linguistics, English and computer science (University of Erlangen-Nuremberg), a Master's in Computer Speech, Text and Internet Technology (University of Cambridge) and a PhD in Informatics (University of Edinburgh), which won the first ACM SIGIR Doctoral Consortium Award. He is a scientific expert for the European Commission (FP7, H2020, Horizon Europe) and other funding bodies in Germany, Austria, the UK and the USA. He also is a past chair of the Microsoft-BCS/BCS IRSG Karen Sparck Jones award. Professor Leidner is an author or co-author of several dozen peer-reviewed publications (including one best paper award), has authored or co-edited two books and holds several patents in the areas of information retrieval, natural language processing, and mobile computing. He has been twice winner of the Thomson Reuters inventor of the year award for the best patent application, and is the past received of a Royal Society of Edinburgh Enterprise Fellowship in Electronic Markets.
Visual Storytelling with Question-Answer Plans
Mirella Lapata, University of Edinburgh, Scotland
Abstract: Visual storytelling aims to generate compelling narratives from image sequences. Existing models often focus on enhancing the representation of the image sequence, e.g., with external knowledge sources or advanced graph structures. Despite recent progress, the stories are often repetitive, illogical, and lacking in detail. To mitigate these issues, we present a novel framework which integrates visual representations with pretrained language models and planning. Our model translates the image sequence into a visual prefix, a sequence of continuous embeddings which language models can interpret. It also leverages a sequence of question-answer pairs as a blueprint plan for selecting salient visual concepts and determining how they should be assembled into a narrative. Automatic and human evaluation on the VIST benchmark (Huang et al., 2016) demonstrates that blueprint-based models generate stories that are more coherent, interesting, and natural compared to competitive baselines and state-of-the-art systems.
Bio: Professor Mirella Lapata is a faculty member in the School of Informatics at the University of Edinburgh. She is affiliated with the Institute for Communicating and Collaborative Systems and the Edinburgh Natural Language Processing Group. Her research centers on computational models for the representation, extraction, and generation of semantic information from structured and unstructured data. This encompasses various modalities, including text, images, video, and large-scale knowledge bases. Prof. Lapata has contributed to diverse applied Natural Language Processing (NLP) tasks, such as semantic parsing, semantic role labeling, discourse coherence, summarization, text simplification, concept-to-text generation, and question answering. Using primarily probabilistic generative models, she has employed computational models to investigate aspects of human cognition, including learning concepts, judging similarity, forming perceptual representations, and learning word meanings. The overarching objective of her research is to empower computers to comprehend requests, execute actions based on them, process and aggregate large datasets, and convey information derived from them. Central to these endeavors are models designed for extracting and representing meaning from natural language text, internally storing meanings, and leveraging stored meanings to deduce further consequences.
++ Organizing committee ++
Ricardo Campos (INESC TEC; University of Beira Interior, Covilhã, Portugal)
Alípio M. Jorge (INESC TEC; University of Porto, Portugal)
Adam Jatowt (University of Innsbruck, Austria)
Sumit Bhatia (Media and Data Science Research Lab, Adobe)
Marina Litvak (Shamoon Academic College of Engineering, Israel)
++ Proceedings Chair ++
João Paulo Cordeiro (INESC TEC & Universidade da Beira do Interior)
Conceição Rocha (INESC TEC)
++ Web and Dissemination Chair ++
Hugo Sousa (INESC TEC & University of Porto)
Behrooz Mansouri (Rochester Institute of Technology)
++ Program Committee ++
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, Japan)
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)
Brucce dos Santos (Computational Intelligence Laboratory (LABIC) - ICMC/USP)
David Semedo (Universidade NOVA de Lisboa)
Deya Banisakher (Florida International University)
Dhruv Gupta (Norwegian University of Science and Technology)
Evelin Amorim (INESC TEC)
Henrique Lopes Cardoso (LIACC & University of Porto)
Ignatius Ezeani (Lancaster University)
Irina Rabaev (Shamoon College of Engineering)
Ismail Altingovde (Middle East Technical University)
João Paulo Cordeiro (INESC TEC & University of Beira Interior)
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)
Mariana Caravanti (Computational Intelligence Laboratory (LABIC) - ICMC/USP)
Moreno La Quatra (Kore University of Enna)
Natalia Vanetik (Sami Shamoon College of Engineering)
Nuno Guimarães (INESC TEC & University of Porto)
Pablo Gervás (Universidad Complutense de Madrid)
Paulo Quaresma (Universidade de Évora)
Purificação Silvano (CLUP & University of Porto)
Ross Purves (University of Zurich)
Satya Almasian (Heidelberg University)
Sérgio Nunes (INESC TEC & University of Porto)
Sriharsh Bhyravajjula (University of Washington)
Udo Kruschwitz (University of Regensburg)
Valentina Bartalesi (ISTI-CNR, Italy)
++ Contacts ++
Website: https://text2story24.inesctec.pt<https://text2story24.inesctec.pt/>
For general inquiries regarding the workshop, reach the organizers at: text2story2024 at easychair.org<mailto:text2story2024 at easychair.org>
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