EVENT REPORT

Report on the Third International Workshop on
Narrative Extraction from Texts(Text2Story 2020)
Ricardo Campos
INESC TEC, Portugal
Ci2 - Smart Cities Research Center, Polytechnic Institute of Tomar, Portugal
ricardo.campos@ipt.pt
Alı́pio M. Jorge
INESC TEC, Portugal
FCUP - University of Porto, Portugal
Sumit Bhatia
IBM Research AI, India

Arian Pasquali
INESC TEC, PortugalJoão Paulo Cordeiro
INESC TEC, Portugal
University of Beira Interior, Portugal
Behrooz Mansouri
Rochester Institute of Technology,
Rochester, NY, USA

Adam Jatowt
Kyoto University, Japan

Conceição Rocha
INESC TEC, Portugal

Brenda Santana
INESC TEC, Portugal
FCUP - University of Porto, Portugal

Prologue
The Third International Workshop on Narrative Extraction from Texts (Text2Story’20 [https://text2story20.inesctec.pt/]) was held on the 14th of April 2020, in conjunction withthe 42nd European Conference on Information Retrieval (ECIR 2020). This year due to the
Covid-19 outbreak the Text2Story workshop was held online on Zoom platform. During thecourse of the day, an average of more than 60 attendees had the opportunity to follow-up anddiscuss the recent advances in extraction and formal representation of narratives. The workshop consisted of two invited keynotes and thirteen paper presentations. The proceedings of
the workshop are available online at http://ceur-ws.org/Vol-2593/

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1

Background

The Text2Story series Workshops is aimed at bringing together researchers from diverse, related
fields such as IR, NLP, AI, design and visualization to come up and share the recent advancesin their respective fields towards narrative understanding. Building upon the success of the past
two workshops [Jorge et al., 2018, 2019a] and on the Text2Story Special Issue at IPM Journal[Jorge et al., 2019b], this year, we organized the third edition, held online under the umbrellaof ECIR 2020. This report, summarizes the key activities at the workshop. The Program
Chairs were Ricardo Campos, Alı́pio Jorge, Adam Jatowt and Sumit Bhatia. The proceedings were setup by our Proceedings Chairs João Paulo Cordeiro and Conceição Rocha. Arian
Pasquali was our Web Chair, Behrooz Mansouri the dissemination chair and Brenda Santana
our research volunteer during the workshop event. The following directs the interested reader
for the online proceedings, presentations and recordings of the talks given at the workshop.
YouTube: https://www.youtube.com/channel/UC8ysv2TEpQZz_-v5T5nZSSA/videos
Presentation Slides: https://text2story20.inesctec.pt/text2story2020_slides.zip
Online Proceedings: http://ceur-ws.org/Vol-2593/

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The Plot

The workshop program consisted of two invited keynote talks, ten regular research papers, twoposition papers and one demonstration. The papers presented at the workshop covered diverse
aspects of the narrative extraction problem ranging from applications of deep learning to narrative
extraction, sentiment analysis, event-correference resolution to applications in different domains
such as legal text, news and social media.

2.1

Keynotes

Sebastião Miranda, head of development at Priberam1 , talked about monitoring multi-lingual,multi-media sources of information to discover relevant stories, events, and topics of interest toa specific user. He presented the solutions developed at Priberam for this purpose by training
text retrieval models and integrating user feedback in the media monitoring pipeline. Mark
Finlayson, assistant professor at School of Computing and Information Sciences, Florida - US2 ,presented an overview of recent advances in Natural Language Processing applied to the task ofnarrative analysis in text. The techniques covered in his talk were focused on four major tasks
for narrative analysis: (1) story detection; (2) character detection; (3) event detection; and (4)timeline extraction.

2.2

Narrative Detection and Extraction

Kanjirangat et al. [2020] presented their work on utilizing deep learning based approaches fornarrative text understanding. They proposed a transformer based classifier to capture static
1
2

https://www.priberam.com/
https://www.fiu.edu/

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relations between different characters of a narrative and dynamic word embeddings to reflect thetemporal evolution of relations between the different characters. Lee and Kim [2020] presented
their work on assessing the fluency of a narrative extracted from textual data. They considered
two aspects of fluency in narratives – consistency, in terms of topics and entities covered; andrapidity, in terms of the pace at which the narrative changes. Lyu et al. [2020] described how
contextual information and external knowledge can be utilized to improve sentiment analysis ofnarrative text.
Effective narrative extraction from texts require identification and coreference resolution of
events mentioned in different documents. Bugert et al. [2020] described an approach to annotate
documents with cross-subtopics event coreference links in a scalable fashion and present a Football
Coreference Corpus (FCC) created following their approach. The corpus consists of 451 sports
news reports spanning over 15,000 sentences and covering 217 events.

2.3

Applications

Elsafoury [2020] presented a case study exploring the use of Twitter for identifying actions andevents by authorities aimed at curbing protests during the Turkish Gezi Park protests in 2013. The
authors also make available a crowd-sourced dataset to enable further research on this important
problem.
Hausner et al. [2020] presented a framework to construct timelines for court cases by extracting
key temporal phrases from court case documents. Such timelines can provide useful insights about
the chronological evolution of court cases and different entities involved. Bandeli et al. [2020]
presented a framework for narrative extraction in blogs where the text is diverse (created byindividual contributors) and potentially noisy, due to lack of any editorial policies.
Berhe et al. [2020] discussed the problem of establishing links between different scenes in aseries of multi-media collection (such as a TV series) for better organization of the content. For
example, a story arc could be spread across multiple episodes or even different seasons. They
present a dataset with hand annotated links between different scenes of the first two seasons of
the TV series Game of Thrones. The dataset consists of 444 scenes involving 154 charactersorganized into 46 stories.
One major application of Narrative extraction from text is in analysis of news and event data
as it can help the readers get an overview of an evolving event from multiple sources. To thisend, Linger and Hajaiej [2020] described their system for clustering multi-lingual news articles andarranging them into coherent stories for easier consumption by the readers. Chakraborty et al.
[2020] presented an approach for constructing a network of key characters involved in an even andrelationships between them. Finally, Piskorski et al. [2020] demonstrated their tool for browsing
and visualizing event-centric timelines from news articles. Given a target entity of interest, thesystem extracts information about events involving the entity from a stream of news articles.

2.4

Looking Ahead

The menace of fake news and misinformation has proliferated our digital lives and the techniques
used for spreading such malicious information are becoming increasingly complex. Often, sucharticles present information that may be factually correct, but is presented using subtle tools such
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as framing, selective reporting, and argument distortions to manipulate the reader. Mensio et al.
[2020] proposed an integrated framework to identify such subtle clues and narrative signals in text
and highlighted the differences in narratives as presented by different news sources.
Along similar lines, Suchanek [2020] posited that the current representation of facts about theWorld in Knowledge Graphs lacks the necessary details to handle complex tasks. He argued forthe need to develop better information extraction tools to augment existing knowledge bases by
incorporating richer meta-data such as relations of higher granularity, evidences and supporting
facts for information stored in the knowledge bases and dependencies between different events.
Access to such a rich knowledge base has the potential to improve fake news detection, controversy
modeling, detecting frauds, and, in general, better understanding of text.

3

The Characters

Due to Covid-19 outbreak, the Text2Story workshop was run fully online this year through zoom
platform (see Figure 1). This new realm forced a quick adaptation of the organization who had
to fit a program to accommodate the different time-zones of presenters and session chairs. We
owe the workshop success to the diverse set of participants, from academia and industry, whoestablished a new record of more than 60 attendees (on average) during all day. The enthusiasm
and active participation of the attendees, ensured that the virtual edition of the workshop was asintellectually stimulating as the past two physical editions.

Figure 1: Overview of the Text2Story2020 Online Workshop
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The workshop program included a total of 13 research papers (out of 20 submissions) from
37 authors and two invited keynote talks. In addition to this, we had six session chairs whobrilliantly moderated the presentation of the papers. Our thanks go to Arian Pasquali (INESC
TEC), Ismail Sengor Altingovde (Middle East Technical University), Jeremy Pickens (OpenText),
Marc Spaniol (Université de Caen Normandie), Paulo Quaresma (Universityof Évora) and Satya
Almasian (Heidelberg University). Finally, we would like to acknowledge the effort and valuable
contribution of the researchers and industry experts that have served on the Program Committeeof the Text2Story’20 workshop. Our thanks go to:• Álvaro Figueira (INESC TEC & University of Porto)• Andreas Spitz (École polytechnique fédérale de Lausanne)• António Horta Branco (University of Lisbon)• Arian Pasquali (INESC TEC)
• Bruno Martins (IST and INESC-ID - Instituto Superior Técnico, University of Lisbon)• Daniel Gomes (FCT/Arquivo.pt)• Daniel Loureiro (University of Porto)• Denilson Barbosa (University of Alberta)• Dhruv Gupta (Max Planck Institute for Informatics)• Dwaipayan Roy (ISI Kolkata, India)• Dyaa Albakour (Signal)• Gaël Dias (Normandie University)• Henrique Lopes Cardoso (University of Porto)• Ismail Sengor Altingovde (Middle East Technical University)• Jeffery Ansah (BHP)• Jeremy Pickens (OpenText)• João Magalhães (Universidade Nova de Lisboa)• Kiran Kumar Bandeli (Walmart Inc.)• Ludovic Moncla (INSA Lyon)• Marc Spaniol (Université de Caen Normandie)• Mark Finlayson (Florida International University)

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Vol. 54 No. 1 June 2020• Mengdie Zhuang (The University of Sheffield)• Nina Tahmasebi (University of Gothenburg)• Nuno Moniz (LIAAD/INESC TEC)
• Pablo Gamallo (University of Santiago de Compostela)• Paulo Quaresma (University of Évora)• Preslav Nakov (Qatar Computing Research Institute (QCRI))• Ross Purves (University of Zurich)• Satya Almasian (Heidelberg University)• Sebastiao Miranda (Priberam)• Sérgio Nunes (INESC TEC & University of Porto)• Udo Kruschwitz (University of Essex)• Vı́tor Mangaravite (UFMG)• Yihong Zhang (Kyoto University)

4

Applause

As part of an effort to promote top quality research, we decided to establish two prizes, the best
paper award and the recognized reviewer award. The first is granted for the best paper publishedand presented at the Text2Story workshop. The latter, aims to recognize reviewers by the theireffort and the quality put on this noble task. The program chairs are pleased to announce that theBest Paper Award of the Text2Story 2020 Workshop went to Vani Kanjirangat, Simone Mellace
and Alessandro Antonucci for their paper ”Temporal Embeddings and Transformer Models for
Narrative Text Understanding” [Kanjirangat et al., 2020]. In addition to this, the program chairs
have recognized the following persons as recipients of the 2020 reviewer award for their insightful
and valuable reviews - Andreas Spitz (École Polytechnique Fédérale de Lausanne), Bruno Martins(IST and INESC-ID - Instituto Superior Técnico, University of Lisbon), Ismail Sengor Altingovde(Middle East Technical University), Mark Finlayson (Florida International University) and Satya
Almasian (Heidelberg University).

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Epilogue

This was the third edition of the Text2Story workshop series. Our objective was to bring together
the interested participants from different geographies and research expertise to collectively set
the agenda for the emerging multi-disciplinary area of narrative extraction from texts. We havereceived increasingly positive response from the research community and the growing participation
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in the workshops underlines the relevance of the topic. Thus, it is our aim to continue organizing
the workshop series in IR and NLP venues to develop the field further.
Acknowledgements
We would like to thank ECIR 2020 organizers for helping us to organize the workshop, our keynote
speakers, members of the program committee, the paper authors, and the participants. The firsttwo authors of this paper were financed by the ERDF – European Regional Development Fundthrough the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 and by National Funds through the Portuguese funding agency, FCT - Fundaçãopara a Ciência e a Tecnologia within project PTDC/CCI-COM/31857/2017 (NORTE-01-0145FEDER-03185)

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