A wide body of research is concerned with the semantics of narratives, both in terms of understanding narratives and generating fictional narratives and stories. We provide a dataset of summaries to be used as a proxy for entire stories or for the analysis of the summaries themselves. Our dataset consists of a total of 96,831 individual summaries across 29,505 stories. We intend for the dataset to be used for training and evaluation of embedding representations for stories, specifically the stories’ narratives. The summary data is harvested from five different language versions of Wikipedia. Our dataset comes with rich metadata, which we extract from Wikidata, enabling a wide range of applications that operate on story summaries in conjunction with metadata. To set baseline results, we run retrieval experiments on the dataset, exploring the capability of similarity models in retrieving summaries of the same story. For this retrieval, a crucial element is to not place too much emphasis on the named entities, as this can enable retrieval of other summaries for the same work without taking the narrative into account.

We present a novel approach to modeling fictional narratives.The proposed model creates embeddings that represent a story such that similar narratives, that is, reformulations of the same story, will result in similar embeddings.We showcase the prowess of our narrativefocused embeddings on various datasets, exhibiting state-of-the-art performance on multiple retrieval tasks.The embeddings also show promising results on a narrative understanding task.Additionally, we perform an annotationbased evaluation to validate that our introduced computational notion of narrative similarity aligns with human perception.The approach can help to explore vast datasets of stories, with potential applications in recommender systems and in the computational analysis of literature.

This paper paves the way for interpretable and configurable semantic similarity search, by training state-of-the-art models for identifying textual similarity guided by a set of aspects or dimensions. The similarity models are analyzed as to which interpretable dimensions of similarity they place the most emphasis on. We conceptually introduce configurable similarity search for finding documents similar in specific aspects but dissimilar in others. To evaluate the interpretability of these dimensions, we experiment with downstream retrieval tasks using weighted combinations of these dimensions. Configurable similarity search is an invaluable tool for exploring datasets and will certainly be helpful in many applied natural language processing research applications.