Discovering the Geometry of Narratives and their Embedded Storylines

Eduard Hoenkamp

2019

Abstract

Many of us struggle to keep up with fast evolving news stories, viral tweets, or e-mails demanding our attention. Previous studies have tried to contain such overload by reducing the amount of information reaching us, make it easier to cope with the information that does reach us, or help us decide what to do with the information once delivered. Instead, the approach presented here is to mitigate the overload by uncovering and presenting only the information that is worth looking at. We posit that the latter is encapsulated as an underlying storyline that obeys several intuitive cognitive constraints. The paper assesses the efficacy of the two main paradigms of Information Retrieval, the document space model and language modeling, in how well each captures the intuitive idea of a storyline, seen as a stream of topics. The paper formally defines topics as high-dimensional but sparse elements of a (Grassmann) manifold, and storyline as a trajectory connecting these elements. We show how geometric optimization can isolate the storyline from a stationary low dimensional story background. The approach is effective and efficient in producing a compact representation of the information stream, to be subsequently conveyed to the end-user.

Paper Citation

in Harvard Style

Hoenkamp E. (2019). Discovering the Geometry of Narratives and their Embedded Storylines. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR; ISBN 978-989-758-382-7, SciTePress, pages 483-490. DOI: 10.5220/0008356004830490

in Bibtex Style

@conference{kdir19,
author={Eduard Hoenkamp},
title={Discovering the Geometry of Narratives and their Embedded Storylines},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR},
year={2019},
pages={483-490},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008356004830490},
isbn={978-989-758-382-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR
TI - Discovering the Geometry of Narratives and their Embedded Storylines
SN - 978-989-758-382-7
AU - Hoenkamp E.
PY - 2019
SP - 483
EP - 490
DO - 10.5220/0008356004830490
PB - SciTePress