@InProceedings{frermann-szarvas:2017:EMNLP2017,
  author    = {Frermann, Lea  and  Szarvas, Gy\"{o}rgy},
  title     = {Inducing Semantic Micro-Clusters from Deep Multi-View Representations of Novels},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1873--1883},
  abstract  = {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.},
  url       = {https://www.aclweb.org/anthology/D17-1200}
}