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Text2Story 2023 : Sixth International Workshop on Narrative Extraction from Texts


When Apr 2, 2023 - Apr 2, 2023
Where Dublin, Ireland
Submission Deadline Feb 6, 2023
Notification Due Mar 2, 2023
Final Version Due Mar 17, 2023
Categories    NLP   computational linguistics   artificial intelligene

Call For Papers



International Workshop on Narrative Extraction from Texts (Text2Story'23)

in conjunction with the 45th European Conference on Information Retrieval (ECIR'23)

2nd, 2023 - Dublin, Ireland



Important Dates ++

Submission deadline: January 23rd, 2023

Acceptance Notification Date: March 3rd, 2023

Camera-ready copies: March 17th, 2023

Workshop: April 2nd, 2023

Overview ++

Recent years have shown a stream
of continuously evolving information making it unmanageable and time-consuming for an interested reader to track and process and to 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

List of Topics ++

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

Personalization and Recommendation
of Narratives

User Profiling and User Behavior

Sentiment and Opinion Detection
in Texts

Argumentation Analysis

Bias Detection and Removal
in Generated Stories

Ethical and Fair Narrative

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

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

Dataset ++

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. A script to reconstruct and expand the dataset is available at
The article itself is available at this link:

Submission Guidelines ++

invite two kinds of submissions:

Full papers
(up to 7 pages + references): Original and high-quality unpublished contributions on the theory and practical aspects of the narrative extraction task.
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,
and 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.

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 ++

of accepted papers will be given 15 minutes for oral presentations.

Invited Speakers ++
Structured Summarisation of News
at Scale
Ifrim, University College
Dublin, Ireland

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

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.

Creating and Visualising Semantic
Story Maps
Bartalesi, CNR-ISTI,

A narrative is a conceptual
basis of collective human understanding. Humans use stories to represent characters' intentions, feelings and the attributes of objects, and events. A widely-held thesis in psychology to justify the centrality of narrative in human life is that humans make
sense of reality by structuring events into narratives. Therefore, narratives are central to human activity in cultural, scientific, and social areas. Story maps are computer science realizations of narratives based on maps. They are online interactive maps
enriched with text, pictures, videos, and other multimedia information, whose aim is to tell a story over a territory. This talk presents a semi-automatic workflow that, using a CRM-based ontology and the Semantic Web technologies, produces semantic narratives
in the form of story maps (and timelines as an alternative representation) from textual documents. An expert user first assembles one territory-contextual document containing text and images. Then, automatic processes use natural language processing and Wikidata
services to (i) extract entities and geospatial points of interest associated with the territory, (ii) assemble a logically-ordered sequence of events that constitute the narrative, enriched with entities and images, and (iii) openly publish online semantic
story maps and an interoperable Linked Open Data-compliant knowledge base for event exploration and inter-story correlation analyses. Once the story maps are published, the users can review them through a user-friendly web tool. Overall, our workflow complies
with Open Science directives of open publication and multi-discipline support and is appropriate to convey "information going beyond the map" to scientists and the large public. As demonstrations, the talk will show workflow-produced story maps to represent
(i) 23 European rural areas across 16 countries, their value chains and territories, (ii) a Medieval journey, (iii) the history of the legends, biological investigations, and AI-based modelling for habitat discovery of the giant squid Architeuthis dux.

Valentina Bartalesi Lenzi
is a researcher at the CNR-ISTI and external professor of Semantic Web in the Computer Science master's degree course at the University of Pisa. She earned her PhD in Information Engineering from the University of Pisa and graduated in Digital Humanities from
the University of Pisa. Her research fields mainly concern Knowledge Representation, Semantic Web technologies, and the development of formal ontologies for representing textual content and narratives. She has participated in several European and National
research projects, including MINGEI, PARTHENOS, E-RIHS PP, IMAGO. She is the author of over 50 peer-reviewed articles in national and international conferences and scientific journals.

Organizing committee ++

Campos (INESC TEC; Ci2 - Smart Cities Research Center, Polytechnic Institute of Tomar, Tomar, Portugal)

M. Jorge (INESC TEC; University of Porto, Portugal)

Jatowt (University of Innsbruck, Austria)

Bhatia (Media and Data Science Research Lab, Adobe)

Litvak (Shamoon Academic College of Engineering, Israel)

Proceedings Chair ++

Paulo Cordeiro (INESC TEC & Universidade da Beira do Interior)


Web and Dissemination Chair ++

Sousa (INESC TEC & University of Porto)

Mansouri (University of Southern Maine)

Program Committee ++

Figueira (INESC TEC & University of Porto)

Spitz (University of Konstanz)

Doucet (Université de La Rochelle)

Horta Branco (University of Lisbon)

Pasquali (CitizenLab)

Gajderowicz (University of Toronto)

Altuna (Universidad del País Vasco)

Santana (Federal University of Rio Grande do Sul)

Martins (IST & INESC-ID, University of Lisbon)

Loureiro (Cardiff University)

Aumiller (Heidelberg University)

Gupta (Norwegian University of Science and Technology)

Albakour (Signal UK)

Amorim (INESC TEC)

Cardoso (INESC TEC & University of Porto)

Altingovde (Middle East Technical University)

Paulo Cordeiro (INESC TEC & University of Beira Interior)

Bandeli (Walmart Inc.)

Cagliero (Politecnico di Torino)

Moncla (INSA Lyon)

Finlayson (Florida International University)

Spaniol (Université de Caen Normandie)

La Quatra (Politecnico di Torino)

Guimarães (INESC TEC & University of Porto)

Gamallo (University of Santiago de Compostela)

Gervás (Universidad Complutense de Madrid)

Quaresma (Universidade de Évora)

Rayson (Lancaster University)

Jain (Indian Institute of Technology, Patna)

Purves (University of Zurich)

Almasian (Heidelberg University)

Nunes (INESC TEC & University of Porto)

Shahid (Adobe's Media and Data Science Research Lab)

Bhyravajjula (University of Washington)

Kruschwitz (University of Regensburg)

Kocaman (John Snow Labs & Leiden University)

Contacts ++

For general inquiries regarding
the workshop, reach the organizers at:

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