April 2nd, 2023 - Dublin, Ireland

Text2Story 2023

Sixth International Workshop on Narrative Extraction from Texts
held in conjunction with the 45th European Conference on Information Retrieval

Call for papers


Recent years have shown a stream of continuously evolving information making it unmanageable and time-consuming for an interested reader to track and keep up with all the essential information and the various aspects of a story. Automated narrative extraction from text offers a compelling approach to this problem. It involves identifying the sub-set of interconnected raw documents, extracting the critical narrative story elements, and representing them in an adequate final form (e.g., timelines) that conveys the key points of the story in an easy-to-understand format. Although information extraction and natural language processing have made significant progress towards an automatic interpretation of texts, the problem of automated identification and analysis of the different elements of a narrative present in a document (set) still presents significant unsolved challenges.

Call for papers

In the sixth 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 models, as well as in modern architectures (e.g., transformers) 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. To this regard, we encourage the submission of high-quality and original submissions covering the following topics:

  • Narrative Representation Models
  • Story Evolution and Shift Detection
  • Temporal Relation Identification
  • Temporal Reasoning and Ordering of Events
  • Causal Relation Extraction and Arrangement
  • Narrative Summarization
  • Multi-modal Summarization
  • Automatic Timeline Generation
  • Storyline Visualization
  • Comprehension of Generated Narratives and Timelines
  • Big Data Applied to Narrative Extraction
  • Personalization and Recommendation of Narratives
  • User Profiling and User Behavior Modeling
  • Sentiment and Opinion Detection in Texts
  • Argumentation Analysis
  • Bias Detection and Removal in Generated Stories
  • Ethical and Fair Narrative Generation
  • Misinformation and Fact Checking
  • Bots Influence
  • Narrative-focused Search in Text Collections
  • Event and Entity importance Estimation in Narratives
  • Multilinguality: Multilingual and Cross-lingual Narrative Analysis
  • Evaluation Methodologies for Narrative Extraction
  • Resources and Dataset Showcase
  • Dataset Annotation for Narrative Generation/Analysis
  • Applications in Social Media (e.g. narrative generation during a natural disaster)
  • Language Models and Transfer Learning in Narrative Analysis
  • Narrative Analysis in Low-resource Languages

tls-covid19 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 including the study of news coverage about the COVID-19 pandemic.

Important Dates


We invite two kinds of submissions:

Full Papers

up to 7 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.

Work in Progress | Demos | Dissemination Papers

up to 4 pages + references

Unpublished short papers describing work in progress; demo; and resource papers presenting research/industrial prototypes, datasets or software packages; 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.

Papers must be submitted electronically in PDF format through Easy Chair . All submissions must be in English and formatted according to the one-column CEUR-ART style with no page numbers. Templates, either in Word or LaTeX, can be found in the following zip folder . There is also an Overleaf page for LaTeX users.

IMPORTANT: Please include between brackets the type of submission (full; negative results; work in progress; demo and resource; position; dissemination) in the paper title.

Papers submitted to Text2Story 2023 should be original work and different from papers that have been previously published, accepted for publication, or that are under review at other venues. Exceptions to this rule are "dissemination papers". Pre-prints submitted to ArXiv are eligible.

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.


Organizing Committee

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)
  • Arian Pasquali (CitizenLab)
  • Bart Gajderowicz (University of Toronto)
  • Begoña Altuna (Universidad del País Vasco)
  • 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 Cardoso (INESC TEC & University of Porto)
  • Ismail Altingovde (Middle East Technical University)
  • João Paulo Cordeiro (INESC TEC & University of Beira Interior)
  • Kiran Bandeli (Walmart Inc.)
  • Luca Cagliero (Politecnico di Torino)
  • Ludovic Moncla (INSA Lyon)
  • Marc Finlayson (Florida International University)
  • Marc Spaniol (Université de Caen Normandie)
  • Moreno La Quatra (Politecnico di Torino)
  • 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)
  • 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)
  • Veysel Kocaman (John Snow Labs & Leiden University)

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)

Invited Speakers

Structured Summarisation of News at Scale

Speaker: Georgiana Ifrim, University College Dublin, Ireland

Abstract: Facilitating news consumption at scale is still quite challenging. Some research effort focused on coming up with useful structures for facilitating news navigation for humans, but benchmarks and objective evaluation of such structures is not common. One area that has progressed recently is news timeline summarisation. In this talk, we present some of our work on long-range large-scale news timeline summarisation. Timelines present the most important events of a topic linearly in chronological order and are commonly used by news editors to organise long-ranging topics for news consumers. Tools for automatic timeline summarisation can address the cost of manual effort and the infeasibility of manually covering many topics, over long time periods and massive news corpora. In this talk, we first compare different high-level approaches to timeline summarisation, identify the modules and features important for this task, and present new state-of-the-art results with a simple new method. We provide several examples of automatic timelines and present both a quantitative and qualitative analysis of these structured news summaries. Most of our tools and datasets are available online on github.

Bio: Dr. Georgiana Ifrim is an Associate Professor at the School of Computer Science, UCD, co-lead of the SFI Centre for Research Training in Machine Learning (ML-Labs) and SFI Funded Investigator with the Insight Centre for Data Analytics and VistaMilk SFI Centre. Dr. Ifrim holds a PhD and MSc in Machine Learning, from Max-Planck Institute for Informatics, Germany, and a BSc in Computer Science, from University of Bucharest, Romania. Her research focuses on effective approaches for large-scale sequence learning, time series classification, and text mining. She has published more than 50 peer-reviewed articles in top-ranked international journals and conferences and regularly holds senior positions in the program committees for IJCAI, AAAI, and ECML-PKDD, as well as being a member of the editorial board of the Machine Learning Journal, Springer.


Text2Story 2023 will be held at the 45th European Conference on Information Retrieval (ECIR'23) in Dublin, Ireland


This project is financed by the ERDF – European Regional Development Fund through the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project PTDC/CCI-COM/31857/2017 (NORTE-01-0145-FEDER-03185)