Inducing Semantic Micro-Clusters from Deep Multi-View Representations of Novels

By Lea Frermann, György Szarvas. 2017

11 citations recorded by the source corpus.

Automatically understanding the plot of novels is important both for informing literary scholarship and applications such as summarization or recommendation. Various models have addressed this task, but their evaluation has remained largely intrinsic and qualitative. Here, we propose a principled and scalable framework leveraging expert-provided semantic tags (e.g., mystery, pirates) to evaluate plot representations in an extrinsic fashion, assessing their ability to produce locally coherent groupings of novels (micro-clusters) in model space. We present a deep recurrent autoencoder model that learns richly structured multi-view plot representations, and show that they i) yield better micro-clusters than less structured representations; and ii) are interpretable, and thus useful for further literary analysis or labeling of the emerging micro-clusters.

Subfields: Artificial Intelligence, General Social Sciences, Natural Language Processing, Representation & Self-Supervised Learning

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Citations in the corpus

The NarrativeQA Reading Comprehension Challenge (2017, background): “These include high level plot understanding through clustering of novels (Frermann and Szarvas, 2017) or summarization of movie scripts (Gorinski and Lapata, 2015), to more fine grained processing by inducing character types (Bamman et al., 2014b; Bamman et al., 2014a), understanding relationships…”