[Event at CIG] Text2Story at ECIR’23 Call for Participation Inbox

Thu Jan 5 19:34:33 CET 2023

*** Apologies for cross-posting ***

++ CALL FOR PAPERS ++

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

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

April 2nd, 2023 - Dublin, Ireland

Website: https://text2story23.inesctec.pt

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++ 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 ++

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

++ 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. 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 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. 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 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.

    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 ++
Structured Summarisation of News at ScaleSpeaker: Georgiana Ifrim
<https://people.ucd.ie/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 <https://github.com/complementizer/news-tls>.

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.

Creating and Visualising Semantic Story MapsSpeaker: Valentina Bartalesi
<https://scholar.google.it/citations?user=EnxyxO0AAAAJ&hl=en>, CNR-ISTI,
Italy

Abstract: 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.

Bio: 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 ++

    Ricardo Campos (INESC TEC; Ci2 - Smart Cities Research Center,
Polytechnic Institute of Tomar, Tomar, 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 (University of Southern Maine)

++ 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)

    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)

    Veysel Kocaman (John Snow Labs & Leiden University)

++ Contacts ++

    Website: https://text2story23.inesctec.pt
    For general inquiries regarding the workshop, reach the organizers at:
text2story2023 at easychair.org