April 22 – 25 , 2019, Dagstuhl Seminar 19172

Computational Creativity Meets Digital Literary Studies


Tarek Richard Besold (Telefonica Innovacion Alpha – Barcelona, ES)
Pablo Gervás (Complutense University of Madrid, ES)
Evelyn Gius (TU Darmstadt, DE)
Sarah Schulz (Ada Health – Berlin, DE)

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Dagstuhl Report, Volume 9, Issue 4 Dagstuhl Report
Aims & Scope
List of Participants
Dagstuhl's Impact: Documents available
Dagstuhl Seminar Schedule [pdf]


Literary studies (LS) is a subfield of the humanities that provides a diversity of possible views on its objects of investigation. The universal approach to literary texts does not exist, instead there are many, sometimes incompatible theories that can be applied for the interpretation of literary texts. Additionally, with the emerging of the Digital Humanities (DH) the deployment of computational methods has been introduced into LS, leading to a further expansion of the range of theories and methodologies of text analysis and interpretation. Against that backdrop in the last decade much effort has especially been put into developing approaches that cover rather complex concepts for text analysis, including, among other, network theory (e.g., [7]) and approaches from distributional semantics for topic modelling and word vector estimation (e.g., [8]). This considerably changed the prerequisites of DH research in the field of LS. In many cases it is no longer possible to simply apply a predefined tool or algorithm, requiring traditionally trained LS scholars to move away from their disciplinary paradigm of individual research and towards adapting collaborative modes that can provide both LS and computational expertise. Researchers in Natural language processing (NLP) have shown considerable interest in text-based DH research. This interest is not only motivated by the diversity and complexity of the research questions, which offers an ideal testbed for the development of new methods and combined workflows, but also by the nature of texts found in the context of these research questions which are often diverse with respect to their lexical and syntactic range – meeting the need for this type of data in work aiming for more flexible NLP approaches. Computational Creativity (CC) is a multidisciplinary endeavour, modelling, simulating or replicating aspects of creativity using a computer, in order to achieve one of several ends: Either to construct a program or computer capable of human-level creativity, or to better understand human creativity and to formulate an algorithmic perspective on creative behaviour in humans, or to design programs that can enhance human creativity without necessarily being creative themselves (a concise overview of the main aspects of the field has, for instance, been laid out by [1]). One of CC’s most popular subfields is Computational Storytelling (CS), where researchers hitherto have mainly thought about the structure and logical implications of building blocks of stories, leaving most other dimensions of narrative construction out of consideration.

Taking stock of this overall state of affairs and the specific situation in the respective fields, the seminar was constructed around several main challenges:

  • One of the major challenges in DLS is the approximation of concepts with computational approaches to, i.e. their operationalization, that not only requires a translation of the concepts, but also a deep understanding of the deployed computational approaches used. This gap can be tackled best by providing expertise from the fields concerned. Whereas NLP is already accepted as such a field (but still needed), CS has not been taken much into consideration yet. A second type of collaboration that still needs to be intensified is the one that connects the interpretative, manual annotations from DLS (e.g., [4]) with computational approaches to text analysis and generation.
  • NLP has focused on a limited variety of texts in its beginnings and suffers from a bias towards newspaper texts. Even though there are efforts towards more diverse and flexible text processing, the constant lack of data is a problem. DH and CC offer a variety of texts to improve this situation – but are hitherto underused in that capacity.
  • CC, CS focuses almost exclusively on plot and logical structure of storytelling. However, a narrative is a complex web of different factors that are well-investigated in classical disciplines. While much work is based on formalist theories about narrative (especially [9]), other approaches from narrative theory still need to be explored better. For example, CS could benefit from the well-established fields of semiotics (e.g., [5]) and structuralism (e.g., [3]) as well as from more recent, reader-oriented developments in cognitive and empirical narratology (e.g. [6]; [2]).

In order to make researchers from the participating communities a) aware of the challenges and the corresponding opportunities an interdisciplinary meeting like the seminar offered, and b) make them take advantage of these opportunities still on-site, the seminar was split between presentations from researchers describing their recent work and questions they wanted to highlight for the audience, and ``hackathon'' phases in which decidedly interdisciplinary teams of participants worked on concrete projects.

The following pages summarize the content of these presentations and the outcomes of the group projects.


  1. M. A. Boden. How computational creativity began. In M. Besold, T. R.; Schorlemmer and A. Smaill, editors, Computational Creativity Research: Towards Creative Machines. Atlantis Press, Amsterdam, 2015.
  2. M. Bortolussi, P. Dixon, and F. C. E.P. Dixon. Psychonarratology: Foundations for the Empirical Study of Literary Response. Psychonarratology: Foundations for the Empirical Study of Literary Response. Cambridge University Press, 2003.
  3. G. Genette. Narrative discourse:. G – Reference, Information and Interdisciplinary Subjects Series. Cornell University Press, 1980.
  4. E. Gius. In Diegesis, page 4. 2016.
  5. A. J. Greimas. Structural Semantics: An Attempt at a Method. University of Nebraska Press, 1983.
  6. D. Herman. Story Logic: Problems and Possibilities of Narrative. Frontiers of narrative. University of Nebraska Press, 2002.
  7. F. Moretti. Network Theory, Plot Analysis. Literary lab. Stanford Literary Lab, 2011.
  8. Christof Schöch. Topic Modeling Genre: An Exploration of French Classical nd Enlightenment Drama. Digital Humanities Quarterly, November 2016. This is a pre-publication version of an article to appear in Digital Humanities Quarterly. Last revision: October 2016.
  9. Vladimir Propp. Morphology of the Folktale.University of Texas Press, 2010.
Summary text license
  Creative Commons BY 3.0 Unported license
  Sarah Schulz, Tarek Richard Besold, Pablo Gervás, and Evelyn Gius


  • Artificial Intelligence / Robotics
  • Multimedia
  • Society / Human-computer Interaction


  • Computational creativity
  • Storytelling
  • Digital humanities
  • Digital literary studies
  • Computational narrativity


In the series Dagstuhl Reports each Dagstuhl Seminar and Dagstuhl Perspectives Workshop is documented. The seminar organizers, in cooperation with the collector, prepare a report that includes contributions from the participants' talks together with a summary of the seminar.


Download overview leaflet (PDF).

Dagstuhl's Impact

Please inform us when a publication was published as a result from your seminar. These publications are listed in the category Dagstuhl's Impact and are presented on a special shelf on the ground floor of the library.


Furthermore, a comprehensive peer-reviewed collection of research papers can be published in the series Dagstuhl Follow-Ups.