We present the shared task on narrative similarity and narrative representation learning — NSNRL (pronounced "nass-na-rel").The task operationalizes narrative similarity as a binary classification problem: determining which of two stories is more similar to an anchor story.We introduce a novel definition of narrative similarity, compatible with both narrative theory and intuitive judgment.Based on the similarity judgments collected under this concept, we also evaluate narrative embedding representations.We collected at least two annotations each for more than 1,000 story summary triples, with each annotation being backed by at least two annotators in agreement.This paper describes the sampling and annotation process for the dataset; further, we give an overview of the submitted systems and the techniques they employ.We received a total of 71 final submissions from 46 teams across our two tracks.In our triple-based classification setup, LLM ensembles make up many of the top-scoring systems, while in the embedding setup, systems with pre- and post-processing on pretrained embedding models perform about on par with custom fine-tuned solutions.Our analysis identifies potential headroom for improvement of automated systems in both tracks.The task website includes visualizations of embeddings alongside instance-level classification results for all teams.

We present the novel dataset GermAnProse, an annotated corpus consisting of four German short prose texts accompanied by an extensive set of narrative-focused annotations.As part of this dataset, we contribute an annotation scheme for mentions, speech, and character agency: Characters in Action (ChiA).GermAnProse also contains information on narrative phenomena: narrativity, semantic verb classes, and plot keyness.Moreover, we include reader reception data in the form of timing information for audiobook performances, indicating pauses between sentences and the time taken to read a specific sentence in a performance.We release the dataset, which contains more than 18,000 manually created standoff annotations in JSON format, enabling researchers to utilize this resource for further exploratory applications.

Automatizing the process of understanding the global narrative structure of long texts and stories is still a major challenge for state-of-the-art natural language understanding systems, particularly because annotated data is scarce and existing annotation workflows do not scale well to the annotation of complex narrative phenomena. In this work, we focus on the identification of narrative levels in texts corresponding to stories that are embedded in stories. Lacking sufficient pre-annotated training data, we explore a solution to deal with data scarcity that is common in machine learning: the automatic augmentation of an existing small data set of annotated samples with the help of data synthesis. We present a workflow for narrative level detection, that includes the operationalization of the task, a model, and a data augmentation protocol for automatically generating narrative texts annotated with breaks between narrative levels. Our experiments suggest that narrative levels in long text constitute a challenging phenomenon for state-of-the-art NLP models, but generating training data synthetically does improve the prediction results considerably.

This paper introduces the novel task of scene segmentation on narrative texts and provides an annotated corpus, a discussion of the linguistic and narrative properties of the task and baseline experiments towards automatic solutions. A scene here is a segment of the text where time and discourse time are more or less equal, the narration focuses on one action and location and character constellations stay the same. The corpus we describe consists of German-language dime novels (550k tokens) that have been annotated in parallel, achieving an inter-annotator agreement of gamma = 0.7. Baseline experiments using BERT achieve an F1 score of 24%, showing that the task is very challenging. An automatic scene segmentation paves the way towards processing longer narrative texts like tales or novels by breaking them down into smaller, coherent and meaningful parts, which is an important stepping stone towards the reconstruction of plot in Computational Literary Studies but also can serve to improve tasks like coreference resolution.