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\title{Narrative in Short German Prose:\\A Multi-Phenomenon Dataset for Computational Literary Analysis}

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\author{\bf Hans Ole Hatzel\thanks{Equal contribution.}$^1$, Haimo Stiemer\footnotemark[1]$^2$, \\
\bf Evelyn Gius$^2$, Chris Biemann$^1$ \\
University of Hamburg$^1$, TU Darmstadt$^2$\\
\small \texttt{\{hans.ole.hatzel, chris.biemann\}@uni-hamburg.de},  \\
\small \texttt{\{haimo.stiemer,evelyn.gius\}@tu-darmstadt.de}
}
\begin{document}

\maketitle
\begin{abstract}
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.
\end{abstract}

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\section{Introduction}
In recent years, the computational analysis of literature has gained increasing attention, as evidenced by the emerging field of Computational Literary Studies (CLS).
Our novel corpus GermAnProse (short for \textbf{Germ}an \textbf{an}notated \textbf{prose}), contains annotations of a series of narrative phenomena for four literary texts and is meant to serve the field.
Figure \ref{fig:annotation_example} showcases our annotations on a toy example.
Specifically, our dataset contains annotations for narrativity \cite{vauthAutomatedEventAnnotation2021}, character mentions and agency, semantic verb classes \cite{hatzelScalableVerbBasedLiterary2025}, plot keyness \cite{hatzelNarrativitatUndHandlung2023}, scenes \cite{zeheDetectingScenesFiction2021}, and audiobook timing information.
With this publication, we pursue the goal of providing a dataset with rich annotations for performing explorative studies of narrative phenomena with audiobook information, in the form of the time taken to read a sentence and the length of pauses between sentences, serving as a reader-reception-focused extension.
In the established CLS process of operationalization, annotation, automation, and exploration \cite{pichlerReflektierteTextanalyse2020}, we envision our dataset to serve as an initial exploration resource. % add back in this ciation: hatzelMachineLearningComputational2023
Researchers can use the annotated texts to identify interesting phenomena and explore their interaction, guiding their subsequent research on larger corpora.
Moreover, the dataset can serve as ground truth for the development and evaluation of automated approaches.
We see this work as part of a larger trend with drastic improvements in natural language processing, allowing the field to increasingly shift its focus from low-level local linguistic phenomena to higher-level narrative and semantic phenomena.

The dataset is provided in JSON format with text and corresponding character-level standoff annotations, allowing for a low barrier of entry and easy exploration in any scripting environment.\footnote{\url{https://github.com/forTEXT/GermAnProse}}

\begin{figure}
    \includegraphics[width=\linewidth]{img/example-annotation.pdf}
    \caption{In our dataset, phenomena are annotated at a sentence level (plot keyness and reading speed) or at a phrase level (agency, narrativity, and verb classes).
    The roles of characters in a given agency annotation are identified on a span level; our scene annotations are not shown here.
    The example shows an interaction of \textit{Alice} and \textit{Bob} in which \textit{Alice} is the active participant.
    \label{fig:annotation_example}
} 
\end{figure}

\section{Related Work}
Here, we will only discuss prior work that does not integrate into our dataset.
Our dataset contains a range of previously released annotations that we merge into one unified dataset, see Section \ref{sec:existing_annotations} for details on this data.

CLS has a strong tradition of reusable datasets; largely, these datasets focus on providing collections of plain texts or texts in TEI-XML with formatting information in conjunction with limited metadata \cite[][e.g]{gius2021dprose,schochCreatingEuropeanLiterary2021}.
The DraCor initiative \cite{fischerProgrammableCorporaIntroducing2019}, for example, focuses on drama and provides the texts in TEI-XML with metadata, such as publication year, and limited annotations, such as speaker identification. 
Often CLS researchers publish annotations for a single phenomenon of interest on a limited set of project-specific texts \cite[][e.g.]{underwoodWhyLiteraryTime2018,piperDataDrivenTheoryNarrativity2022,collardanuyLivingMachinesStudy2020}.
\citet{hagedornBearingBagofTalesOpen2022} set out to create a dataset of folk tales that is extensible by other researchers.

Audiobooks have received limited attention as a resource in NLP research.
Largely, approaches focused on audiobooks as a speech data source \cite{szekelyEvaluatingExpressiveSpeech2012,boeffardFullyAutomaticAnnotation2012}.
\citet{stiemerPauseImText2025} introduced the idea of using audiobook information as a means of analysis for the original text to the CLS community.
The public domain audiobooks on Librivox\footnote{\url{https://librivox.org}} have long attracted the interest of the research community in the humanities and natural language processing alike \cite{weberReadingPublicDomain2021,beilharzLibriVoxDeEnCorpusGermantoEnglish2020}.



\section{Corpus \& Annotations}
In our selection of texts, we largely follow the selection by \citet{vauthAutomatedEventAnnotation2021}, including representation of modern German literature while relying only on out-of-copyright texts (see Table \ref{tab:annotation_counts} for details).
This selection enables us to build upon their published annotations for our own work. % TODO: this is a anonymization red herring.
Further, for resource reasons, we limit our selection to short narratives with the longest text consisting of just over 22,000 tokens.
We annotate the texts in their entirety, rather than excerpts of long texts like some prior work \cite[][e.g.]{bammanAnnotatedDatasetCoreference2020}; we want to, thereby, enable the analysis of entire self-contained narratives rather than focusing on local linguistic features in service of future automation.
Here, we depart from the text selection by \citet{vauthAutomatedEventAnnotation2021} in removing the annotated excerpt of the much longer text \textit{Effi Briest}, including instead the full text \textit{Der blonde Eckbert}; as a result, all our texts are fully annotated.
The annotated texts originate either from the TextGrid corpus \cite{textgrid2021bibliothek} or the d-Prose 1870-1920 corpus \cite{gius2021dprose}.
In Table \ref{tab:short_names} we provide a full list of the works with relevant metadata.
\begin{table*}
    \centering
    \small
    \begin{tabular}{lrrrrrrr}
        \toprule
        Text                    & Author                                  & Published & Short Name\\
        \midrule                                                           
        Der blonde Eckbert      & Ludwig Tieck                            & 1796      &  Eckbert \\ 
        Das Erdbeben in Chili   & Heinrich von Kleist                     & 1807      &  Erdbeben \\ 
        Krambambuli             & Marie von Ebner-Eschenbach              & 1896      &  Krambambuli \\ 
        Die Verwandlung         & Franz Kafka                             & 1915      &  Verwandlung \\ 
    \bottomrule
    \end{tabular}
    % Key sentences:
    % & 314 
    % & 148 
    % & 172 
    % & 462
    % The data has a lot more EvENTs then we would expect. Very weird. E.g.: 571 events for Krambambuli.
    \caption{We list the four texts in our dataset with their respective publication dates and original author names. For reference, we also supply short-form names that are used to identify the texts in some of our tables.}
    \label{tab:short_names}
    % Number of summaries
    % Das Erdbeben in Chili number of summaries: 14
    % Der blonde Eckbert number of summaries: 2
    % Die Judenbuche number of summaries: 11
    % Die Verwandlung number of summaries: 14
    % Krambambuli number of summaries: 16
\end{table*}

Table \ref{tab:annotation_quality} provides an overview of the source and quality of the annotations we discuss in detail below.

\begin{table}
    \centering
    \begin{tabular}{llc}
        \toprule
         Annotations & Publication & Quality \\
         \midrule
         Verbclasses & \citet{hatzelScalableVerbBasedLiterary2025} & \emoji{person} \\
         Keyness & \citet{hatzelNarrativitatUndHandlung2023} & \emoji{person} \\
         Scenes &  Ours & \emoji{person} \\
         Speech Time & Ours & \emoji{robot} \\
         Agency\textsuperscript{ChiA} & Ours & \emoji{1st-place-medal} \\
         Mentions\textsuperscript{ChiA} & Ours & \emoji{1st-place-medal} \\
         %Participations & Ours & \emoji{1st-place-medal} \\
         Narrativity & \citet{vauthAutomatedEventAnnotation2021} & \emoji{1st-place-medal} \\
         \bottomrule
    \end{tabular}
    \caption{We list the annotation source and quality across the different phenomena annotated in our dataset. \emoji{1st-place-medal} refers to gold data, \emoji{robot} refers to automated annotations, and \emoji{person} refers to single annotator annotations. The ChiA superscript denotes data that is part of our novel annotation scheme.}
    \label{tab:annotation_quality}
\end{table}

\subsection{Pre-existing Data}\label{sec:existing_annotations}
In this section, we describe pre-existing published annotations that are included in our dataset and give a rationale for including each set of annotations.

\subsubsection{Narrativity}
Narrativity or eventfulness refers to the quantification of ``how much'' is happening in a narrative text passage.
Our dataset contains narrativity annotations as operationalized in the EvENT concept by \citet{gius2022eventplot}.
In the approach each finite verb in the texts is annotated as expressing one of four \textit{event} types: change of state, process, state, or non-event.
The event types, in the order listed above, are associated with decreasing narrativity \cite{vauthAutomatedEventAnnotation2021}.
Events are not derived from semantic units -- as the concept is typically understood in natural language processing \cite[see e.g.][]{doddingtonAutomaticContentExtraction2004} -- but are instead informed by their textual representation.
As a result, the concept can be understood as the first step in a bottom-up approach to event semantics, ranging from simple and text surface-based phenomena to more complex semantic phenomena.
Change of state events mark the most eventful moments in a text, while non-events indicate eventlessness (since they do not denote actual events in the fictional world but rather descriptive passages, questions, or counterfactual statements).

\subsubsection{Semantic Verb Classes} The verb class data were generated to provide a coarse-grained semantic profile of individual literary texts based on the meanings of their verbs \cite{hatzelScalableVerbBasedLiterary2025}.
Following the narrativity annotations \cite{vauth2021richtlinien}, verb phrases were used as the annotation unit, with each annotation semantically describing the finite verb in a phrase.
Building on this event-centered segmentation, each main verb was classified according to the semantic field of its lemma in GermaNet, resulting in one of fifteen verb classes such as \textit{communication}, \textit{emotion}, \textit{location}, \textit{change}, or \textit{perception}.
Ambiguous or missing verb senses were resolved through contextual interpretation to maintain internal consistency across each annotated text.
The classes represent broad categories of actions and processes and were selected to capture the central event types that shape narrative meaning. 

For each literary work, \citet{hatzelScalableVerbBasedLiterary2025} aggregated the resulting annotations to produce text-specific verb class distributions.
%They did not perform an extensive exploration of the verb classes on a local level.
The resulting frequency profiles are used as interpretable summaries of a text's semantic structure, highlighting tendencies such as an emphasis on emotional, social, or physical processes.

We expect that the verb classes can be used as a first order approximation of action semantics in exploratory text analysis.

\subsubsection{Plot Keyness} With ``plot keyness'' we refer to a given sentence's relevance to the plot of the overall story. For example, a passage describing the natural beauty of the scenery will, in most cases, not contain the most important actions of the story.
The data captures plot keyness by using a corpus of summaries for measuring how many of them reference a given passage of text \cite{hatzelNarrativitatUndHandlung2023} .
%For example, the sentence ``Josephe recieves the death penalty.'' in one summary references a four sentence passage in the text \textit{Das Erdbeben in Chili} that details the judicial proceedings.
Conceptually, this data builds on the ideas of the Pyramid method \cite{nenkovaEvaluatingContentSelection2004}, asserting that the most relevant events are mentioned in the greatest number of summaries.
For the plot keyness data, we use text passages instead of atomic event units.
In the downstream analysis of our dataset, we expect this to be used to identify low-level textual features for relevance to the overall story.

Specifically, our plot keyness measure is the fraction of summaries in our dataset that reference a given sentence. A value of 1.0 means all summaries refer to a specific sentence, whereas a value of 0.5 means only half of all summaries reference the events in the sentence.

The plot keyness and its relation to concepts like narrativity is a phenomenon of interest for CLS. 
We showcase the exploration of this relationship on our data (see Section \ref{sec:data_exploration}).



\subsection{Novel Annotations}
In this section, we describe new annotations we contribute in this publication that are based on existing annotation schemes.
The newly created annotations are marked as \textit{ours} in Table \ref{tab:annotation_quality}.

\subsection{Scenes}
%We measure keyness by means of sentences being 
To enable the aggregation and contextual analysis of the data compiled in this dataset across selected text segments, we divided the corpus texts into scenes.
The guidelines applied for this purpose \cite{gius2021guidelines} are based on the narratological understanding of a scene as a narrative segment that constitutes a coherent unit of time, space, action, and character constellation—roughly corresponding to a film shot in terms of its continuity.

The guidelines specify how scene boundaries can be identified by examining changes in these four aspects.
They also provide clear criteria for distinguishing between narrative scenes and non-scenic passages such as summaries, reflections, or general descriptions.
To ensure reliable and consistent annotations, the guidelines assign different weights to each aspect, prioritizing the coherence of events and character constellations over the temporal or spatial continuity.

In addition, the guidelines define lexical and structural indicators of scene changes (e.g., ``suddenly'', ``the next morning''), recommend the use of the film-shot analogy as an intuitive test, and explain how to handle transitional or ambiguous passages.
The resulting annotations enable the segmentation of literary texts into meaningful narrative units, thereby allowing for a systematic analysis of plot structure and narrative rhythm across different genres and styles.
Scenes are one of the aspects of our work that can potentially be extended to large corpora.

\subsection{Audiobook Timing}
Audiobook information serves as additional reception-based data in our dataset, allowing us to connect purely narrative phenomena to a simple form of reception representation.
Potentially, readers' interpretations of a text can hint at deeper phenomena, and analyzing the relationship between narrative phenomena and reader presentation is a worthwhile endeavor.
We obtain our data from amateur readers (from the platform Librivox or read by student assistants) and professional readers in commercial audiobooks.
Each text and its audio performances are put through a forced alignment pipeline to obtain token-level timing information.\footnote{\url{https://github.com/feldberlin/timething}}
We break this information down to the sentence level and provide a reading speed comparison as measured against a text-to-speech (TTS) system that is based on public training data \cite{muller_thorsten_2021_5525342}.\footnote{\url{https://github.com/OHF-Voice/piper1-gpl}}
While the speech synthesis has dealt with explicitly modeling speech speed given phonemes \cite{niDurationModelingGlobal2019}, we take a comparatively simple approach in relying on the duration of a single speech generation.
Our approach is potentially sensitive to individual terms that the TTS system fails to reproduce accurately on a story-analysis level.
If such a term is central to a given text and, for example, co-occurs with major events at an increased rate, it is conceivable that the approach could adversely affect downstream analysis.

\subsection{Mentions, Direct Speech and Character Agency}
We developed annotation guidelines for systematically capturing how characters act and interact as agents within narrative texts; the guidelines are referred to as characters in actions (ChiA).
To maintain compatibility with the narrativity and semantic verb class data, we follow the two annotation schemes in regard to the annotation unit.
That is to say, for each verbal phrase, the character actions associated with the verb are modeled.
Our guidelines specify that these actions encompass all forms of activity within the fictional world, including mental and emotional processes, speech, and physically manifested actions.

%Events are operationalized as verb phrases. Following the annotation scheme by \cite{vauth2021richtlinien}, each verbal phrase -- that is a finite verb and its syntactic descendants -- is annotated as one event.
Our annotations chiefly focus on different degrees of agency, describing the extent to which a character actively shapes an event: ranging from intentional and controlled actions (agentive) to involuntary or imagined actions (low-agency), and further to situations in which the character is merely affected by or influenced by events (passive).
By distinguishing between these levels, the guidelines enable the tracing of narrative dynamics and the influence of characters throughout the text.
We refer to these annotations that capture co-presence and the agentivity of characters on a verbal phrase level as the \textbf{agency} annotations.

In addition to character agency, we annotate character mentions in texts.
For the mention extents, we adopt an existing annotation scheme for character mention span extents \cite{krug_figuren}.
Beyond that, we differ from prior work in that we only annotate individual characters (potentially multiple for one span) rather than annotating collective mentions on a group level (e.g. ``the three friends'' would refer to three separate characters in our annotation scheme).
Further, we introduce two special characters \textit{background character} and \textit{crowd} that are used for non-specific or non-reoccurring characters and unspecified groups of people, respectively.
Figure \ref{fig:annotation_example} illustrates a toy example of an active and a passive character.
For real world example from our dataset see Section \ref{sec:real_example}
As illustrated, character mentions are also annotated with a canonical name for the corresponding character, as the mention's surface form does not always unambiguously identify a character.

In addition to character mentions and agency, direct speech is also annotated, and text inside speech acts is not considered for agency annotations.
All three tags are linked to their corresponding characters; in the case of agency, the particular roles (agentive, low-agency, passive) of each character are also annotated.
Taken together, these annotations provide a structured representation of how characters appear, act, and speak, thereby enabling a systematic analysis of their narrative functions and their contribution to plot development.

\subsubsection{Agreement}
For the purposes of calculating inter-annotator agreement, we treat the identification of agentive, low-agency, and passive participants as a binary classification problem for each character in each annotation unit.
In terms of Krippendorff's Alpha, in a weighted average setup, we achieve agreements of up to 0.6 in identifying characters in the agentive class.
For the low-agency and passive classes, by contrast, we observe much lower agreement, ranging from 0.35 to 0.51 (see Table \ref{tab:agreements} for a breakdown by class and text).
Qualitatively speaking, we can attribute many of the errors to the special \textit{background character} and \textit{crowd} characters; we observe that it can be difficult to discern, especially for larger groups of people, who, besides the main characters, are included in an action.
%We attribute this discrepancy to the fact that passive and low-agentive are conceptually harder to distinguish.
%The agreements for both texts, save for \textit{Das Erdbeben in Chili}, the first text we annotated during which the guidelines were still clarified, are generally considered to fall into the range of moderate agreement.\todo{Was it really the first???}

For the mutual accuracy metric, we calculate the accuracy across all pairs of span-wise matching annotations, macro-averaging across annotator pairs.
This results in a perhaps more interpretable measure: showing that annotators agree on the exact set of characters in approximately 75\% of cases.
%\todo{The accuracy is about the same for all texts, but the alpha not so much. It would be nice to know why. Presumably something to do with class count/balance/frequency.}



\begin{table*}
    \small
    \centering
\begin{tabular}{lrrrrrr}
    \toprule
    & \multicolumn{3}{c}{Mutual Accuracy} & \multicolumn{3}{c}{Krippendorff's Alpha} \\
    \cmidrule(lr){2-4} \cmidrule{5-7} 
    & Erdbeben & Verwandlung & Krambambuli & Erdbeben & Verwandlung & Krambambuli \\
    \midrule
    passive & 0.722 & 0.762 & 0.731 & 0.381 & 0.444 & 0.509 \\
    low-agency & 0.684 & 0.653 & 0.674 & 0.354 & 0.443 & 0.409 \\
    agentive & 0.730 & 0.785 & 0.732 & 0.510 & 0.603 & 0.590 \\
    \bottomrule
    \end{tabular}    
    \caption{The agreements for annotating character agency as measured by mutual accuracy and Krippendorff's Alpha, show that generally speaking, the agentive class is annotated most consistently.}
    \label{tab:agreements}
\end{table*}

\subsection{Annotation Workflow}
%Automated EvENT verbal phrase extraction imported into CATMA.
%Annotated in CATMA, auto gold is created from multiple annotations.
%If spans and tags/properties are exactly the same for two annotators we automatically add them to the new gold collection.
%All annotations are reviewed by the authors
Both the scene and ChiA annotations were manually created using the web-based tool CATMA \cite{gius2025catma} in conjunction with the GitMA package \cite{vauth2022gitma} for programmatic processing.
All annotators were students of literary studies and employed as student assistants or were full-time researchers and co-authors of this paper.

For the ChiA guidelines, extensive pilot annotations accompanied by annotator training were performed, iteratively improving the guidelines.
At least two annotators annotated each text, and for all but one text, at least two annotators annotated the text in its entirety, in addition to another annotator creating the gold standard.
Regular coordination meetings were held to discuss and resolve ambiguous annotation cases, which were additionally documented using a dedicated uncertainty tag.
Based on the resulting annotations, gold-standard annotations were produced by an additional annotator who reconciled inconsistencies.
To facilitate this process, we developed a semi-automatic adjudication pipeline that inserted annotations on which both annotators agreed (on both the spans and properties of an annotation) into a new collection in CATMA.
All annotations automatically created in this manner were subsequently carefully reviewed.
We found the approach to vastly speed up the creation of the gold labels.

We have around 3,400 gold agency annotations across the four texts in our dataset.
For each agency annotation in our gold data, 0.40 agentive characters, 0.57 low-agency characters, and 0.30 passive characters are annotated on average; this results in a total of 1.27 characters per annotation, see Figure \ref{fig:participants_dist} for the distribution.
The maximum number of characters participating in one agency annotated phrase in our dataset is nine, occurring in a sentence ``When \textbf{they} arrived in the Dominicans' church'' from \textit{Das Erdbeben in Chili}, in which a large portion of the story's cast is involved.
% Als sie in der Kirche der Dominikaner ankamen

\begin{figure}
    \begin{tikzpicture}
        \begin{axis}[
            ybar, % Creates a vertical bar chart
            % title={Distribution of Participants},
            xlabel={\footnotesize Number of Participants},
            ylabel={\footnotesize Count},
            xtick=data, % Places ticks at each x-coordinate from the data file
            % nodes near coords, % Displays the value on top of each bar
            nodes near coords align={vertical},
            ymin=0,
            ymax=2400,
            enlarge x limits=0.1, % Adds a little padding on the sides of the plot
            nodes near coords,
            height=0.7\linewidth,
            width=\linewidth,
            bar width=13pt,
            cycle list name=seaborn,
        ]
            % The key change is here:
            \addplot table [
                col sep=comma,      % Specifies that the columns are comma-separated
                x=num_participants, % Uses the 'num_participants' column for the x-axis
                y=count             % Uses the 'count' column for the y-axis
            ] {img/participants.csv}; % The path to your data file
        \end{axis}
    \end{tikzpicture}
    \centering
    \caption{The distribution of the number of participants in each agency annotation in our data is shown. Each annotation includes an average of 1.27 participants. Very few annotations have more than three participants.}
    \label{fig:participants_dist}
\end{figure}
In terms of co-occurrence, there is a sizable number (376) of agency annotations with no characters involved, as each verbal phrase is an annotation unit regardless of its semantics.
For example, the phrase ``The weather is nice.'' has no participants.

%Without zero length: {'agentive': 1.1643835616438356, 'low_agentive': 1.249036237471087, 'passive': 1.1697574893009985}
%With zero length: {'agentive': 0.36598493003229277, 'low_agentive': 0.581270182992465, 'passive': 0.2942231790455687}

%Der blonde Eckbert       1205
%Das Erdbeben in Chili    1064
%Krambambuli               628
%Die Verwandlung          2884
%Total                    5781
\begin{table*}[t]
    \centering
    \small
    \begin{tabular}{lrrrrrrr}
        \toprule
        & &\multicolumn{5}{c}{Number of} \\
        \cmidrule(lr){2-8}
        Text                    & Tokens & Sentences & Scenes & Agency    & Mentions & Speech Acts & Narrativity \\
        \midrule  
        Der blonde Eckbert      & 7508   & 314       & 14     & 259       & 1205     & 20          & 930 \\
        Das Erdbeben in Chili   & 6499   & 224       & 14     & 627       & 1064     & 39          & 712 \\
        Krambambuli             & 4573   & 226       & 16     & 477       &  628     & 39          & 571 \\
        Die Verwandlung         & 22299  & 850       & 26     & 1992      & 2884     & 106         & 2363 \\
        \midrule                                                            
        Total                   & 40879  & 1614      & 70     & 3355      & 5781     & 204         & 4576 \\
        \bottomrule
    \end{tabular}
%\todo[inline]{1205 is maybe 1201 in the data, why is there a discrepancy.}
    % Key sentences:
    % & 314 
    % & 148 
    % & 172 
    % & 462
    % The data has a lot more EvENTs then we would expect. Very weird. E.g.: 571 events for Krambambuli.
    \caption{Annotation Overview. All annotation schemes cover each of the four texts; we omit some of the annotation schemes for brevity.}
    \label{tab:annotation_counts}
    % Number of summaries
    % Das Erdbeben in Chili number of summaries: 14
    % Der blonde Eckbert number of summaries: 2
    % Die Judenbuche number of summaries: 11
    % Die Verwandlung number of summaries: 14
    % Krambambuli number of summaries: 16
\end{table*}

\subsection{Example}\label{sec:real_example}
Figure \ref{fig:krambambuli} shows an example of real annotations from our dataset.
Note that the single sentences consist of multiple verbal phrases, each with its own narrativity and participation annotations.
Further, the example illustrates that not every participant must have a surface representation in the phrase in which they appear. The phrase ``ließ aber eine Flasche [\ldots] bringen'', for example has the character \textit{Hopp} perform an action.
In practice, unlike in the visualization, our annotators label character identities rather than their spans.
However, since all character mentions are separately annotated, the corresponding text mention spans---as depicted in our graphics---can still be retrieved.

\begin{figure*}
  \includegraphics[width=\textwidth, clip, trim=0 16cm 1cm 0]{img/krambambuli.pdf}
  \caption{Example annotations for a single sentence from \textit{Krambambuli}. The sentence has a keyness of 0.25 and a reading speed of 1.36 in our data. Translation: ``Mr. Hopp said for the time being not a word of the pleasure that he had found in the dog, but had a bottle of the good Danzig cherry brandy brought, which the Lion-innkeeper carried at that time, and poured for the wanderer diligently.''}
  \label{fig:krambambuli}
\end{figure*}

\section{Data Exploration}\label{sec:data_exploration}
After laying out the components of our dataset, we perform a preliminary analysis of the data.
Table \ref{tab:annotation_counts} shows a range of dataset statistics.

\paragraph{Narrativity and Plot Keyness}
For narrativity, as measured by event scores \cite{vauthAutomatedEventAnnotation2021}, we find a very weak correlation with plot keyness (r=.067, p<0.05).
The results indicate some connection between the two phenomena, but, as captured by our data, there is no clear discriminative value for either variable in predicting the other.
Narratologically speaking, we expect to find a limited correlation as keyness captures plot relevance, where narrativity only captures narration at a local level; yet, one might expect non-narrative passages to only rarely constitute key passages.
\citet{hatzelNarrativitatUndHandlung2023} came to a similar conclusion, based on comparable data.

\paragraph{Semantic Verb Classes and Narrativity}
In Figure \ref{fig:verbclass_event}, we illustrate the distribution of event types in ascending order of narrativity with each semantic verb class.
We find that some classes, particularly  \textit{Stative Verbs}, \textit{Cognition}, and \textit{Emotion}, are associated with lower narrativity.
Perhaps most notably, the classes \textit{Location}, \textit{Communication}, and \textit{Contact} are associated with higher narrativity.

\begin{figure}
    \centering
    \includegraphics[width=\linewidth]{img/verbclass_event.pdf}
    \caption{The event tags are listed in ascending order of narrativity. For each verb class, we show the relative distribution of event tags within its occurrences.}
    \label{fig:verbclass_event}
\end{figure}

\paragraph{Pauses, Reading Speed, and Plot Keyness}
We examine the phenomenon of pauses and reading speed in the audiobook timing information contained in ChiA-Seeds.
The idea was initially explored by \citet{stiemerPauseImText2025}, and we expand on their analysis using additional texts and introduce the concept of reading speed to the analysis.

In our data, professionals make longer pauses than amateur readers, with an average length of 0.87 and 0.64 seconds, respectively.
The two groups read at an equal pace when considering only the sentences themselves, without taking into account the pauses (1.25 times faster than the TTS system for the professional readers and 1.26 times faster for the amateurs).
For all readers in our data, we observe a very small but significant correlation between the pause after a sentence and its plot keyness (r=.05, p<0.05).
The same effect can be observed for the correlation of reading speed and plot keyness, but it does not rise to statistical significance.
Sentences that contain direct speech are also read slightly faster in our dataset (r=.04, p<0.05).
Further work is required to identify potential differences in presentations by amateur readers compared to professional readers.


\paragraph{Character Interaction}
As our character agency annotations capture the co-presence of characters in verbal phrases, we can use the annotations to build a novel kind of character network.
In conjunction with the scene annotations, the data can also be used to create dynamic character networks, changing with each scene.
We show an instance of a character network for a subset of the scenes in \textit{Das Erdbeben in Chili} in Figure \ref{fig:character_networks}.
So far, high-quality data for performing character network visualization on prose texts has not been available, with many prior approaches falling back on using only specific mentions (e.g., explicit names) and their co-occurrence \cite{ketschikNetzwerkanalysenNarrativerTexte2025} or using hybrid models that include coreference resolution with name clustering or additional rules \cite[][e.g.]{perriOneGraphRule2022a}.
\citet{ketschikNetzwerkanalysenNarrativerTexte2025} also points out that many prior approaches fail to account for differences in character mentions, a problem that our data, at least partially, resolves, as direct speech is not included, and our agency data captures character interactions (or, in the case of low-agency, co-presence).


\begin{figure}
    \centering
    \includegraphics[width=\linewidth]{img/scene_1-5.png}
    %\includegraphics[width=\textwidth]{img/scene_12.png}
    \caption{
        In our character networks with agency data, the color of nodes describes character agency, while the colors at the ends of each describe each character's agency in only that specific interaction.
        Here, we show the character network for the first five scenes in \textit{Das Erdbeben in Chili}.}
    \label{fig:character_networks}
\end{figure}

\section{Conclusion}
In this work, we introduce GermAnProse, a novel dataset for exploring narrative phenomena in short narratives.
Our extensive annotations cover a total of over 40,000 tokens, with more than 18,000 manually created span annotations in various categories, out of which more than 9,000 are newly created for this work.
In addition, we provide automated sentence timing information for audiobooks as aligned with the original texts, allowing a form of reader studies to be performed on our data.
% Data is collected as follows:
% annos_for_total = df.copy()[["Scenes", "Participations", "Speech Acts", "Mentions", "EvENTS", "Verbclasses"]]
% print("Total manual span annotatiosn", sum(annos_for_total.sum()))
% annos_for_total_new = df.copy()[["Scenes", "Participations", "Speech Acts", "Mentions"]]
% print("Total novel manual span annotations", sum(annos_for_total_new.sum()))

In our data exploration, we show initial examples of identifying phenomena that warrant further investigation.
We provide a novel dataset allowing for the exploration of narrative texts.

Despite its focus on narrative exploration, we believe that our dataset will also prove useful for established computational approaches such as character coreference resolution \cite{roesigerCoreferenceLiteraryText2018} or the attribution of direct speech \cite{petersen-freyFinegrainedQuotationDetection2024}.
Likewise, we expect that our dataset can aid in the creation of automation for the character agency annotations.
We recommend that all such approaches adopt text-level leave-one-out cross-validation schemes to verify their generalizability.



%\todo[inline]{Limitations and Ethical Considerations do not count against the page limit}
\section{Limitations}
We see the main limitation of our work in the limited size of the dataset.
Resource-wise, creating a much larger dataset would not be feasible.
In our view, the dataset is sufficiently large to enable the identification of potentially interesting effects, but might not support robust quantitative conclusions in most cases.

As mentioned, our audio speech assessments are potentially sensitive to systematic errors in the speech synthesis system we use. Further analysis, comparing across readers and potentially with other speech synthesis approaches, would be beneficial.

\section{Ethical Considerations}
In terms of legal considerations, all primary annotated works are out of copyright.
The audiobooks we incorporated into the analysis are, in part, commercial products.
We only report derived data (i.e., timing information) intended for analysis that does not allow a reconstruction of the original performance.

In terms of gender representation in our dataset, one of four texts is written by a female author (\textit{Krambambuli} by Marie Ebner von Eschenbach).

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%\section{Bibliography}\label{sec:reference}
\bibliography{lrec2026-example,DO_NOT_EDIT_zotero,languageresource}

\end{document}
