Narrative Maps: An Algorithmic Approach to Represent and Extract
Information Narratives
- URL: http://arxiv.org/abs/2009.04508v2
- Date: Mon, 26 Oct 2020 14:38:00 GMT
- Title: Narrative Maps: An Algorithmic Approach to Represent and Extract
Information Narratives
- Authors: Brian Keith and Tanushree Mitra
- Abstract summary: This article combines the theory of narrative representations with the data from modern online systems.
A narrative map representation illustrates the events and stories in the narrative as a series of landmarks and routes on the map.
Our findings have implications for intelligence analysts, computational journalists, and misinformation researchers.
- Score: 6.85316573653194
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Narratives are fundamental to our perception of the world and are pervasive
in all activities that involve the representation of events in time. Yet,
modern online information systems do not incorporate narratives in their
representation of events occurring over time. This article aims to bridge this
gap, combining the theory of narrative representations with the data from
modern online systems. We make three key contributions: a theory-driven
computational representation of narratives, a novel extraction algorithm to
obtain these representations from data, and an evaluation of our approach. In
particular, given the effectiveness of visual metaphors, we employ a route map
metaphor to design a narrative map representation. The narrative map
representation illustrates the events and stories in the narrative as a series
of landmarks and routes on the map. Each element of our representation is
backed by a corresponding element from formal narrative theory, thus providing
a solid theoretical background to our method. Our approach extracts the
underlying graph structure of the narrative map using a novel optimization
technique focused on maximizing coherence while respecting structural and
coverage constraints. We showcase the effectiveness of our approach by
performing a user evaluation to assess the quality of the representation,
metaphor, and visualization. Evaluation results indicate that the Narrative Map
representation is a powerful method to communicate complex narratives to
individuals. Our findings have implications for intelligence analysts,
computational journalists, and misinformation researchers.
Related papers
- Evaluating the Ability of Computationally Extracted Narrative Maps to Encode Media Framing [1.2277343096128712]
This article explores the capabilities of a specific narrative extraction and representation approach -- narrative maps.
Our results highlight the potential of narrative maps to provide users with insights into the intricate framing dynamics within news narratives.
However, we note that directly leveraging framing information in the computational narrative extraction process remains an open challenge.
arXiv Detail & Related papers (2024-05-04T14:40:28Z) - Fine-Grained Modeling of Narrative Context: A Coherence Perspective via Retrospective Questions [48.18584733906447]
This work introduces an original and practical paradigm for narrative comprehension, stemming from the characteristics that individual passages within narratives tend to be more cohesively related than isolated.
We propose a fine-grained modeling of narrative context, by formulating a graph dubbed NarCo, which explicitly depicts task-agnostic coherence dependencies.
arXiv Detail & Related papers (2024-02-21T06:14:04Z) - Text-Only Training for Visual Storytelling [107.19873669536523]
We formulate visual storytelling as a visual-conditioned story generation problem.
We propose a text-only training method that separates the learning of cross-modality alignment and story generation.
arXiv Detail & Related papers (2023-08-17T09:32:17Z) - M-SENSE: Modeling Narrative Structure in Short Personal Narratives Using
Protagonist's Mental Representations [14.64546899992196]
We propose the task of automatically detecting prominent elements of the narrative structure by analyzing the role of characters' inferred mental state.
We introduce a STORIES dataset of short personal narratives containing manual annotations of key elements of narrative structure, specifically climax and resolution.
Our model is able to achieve significant improvements in the task of identifying climax and resolution.
arXiv Detail & Related papers (2023-02-18T20:48:02Z) - Mixed Multi-Model Semantic Interaction for Graph-based Narrative
Visualizations [10.193264105560862]
Narrative maps are a visual representation model that can assist analysts to understand narratives.
We present a semantic interaction framework for narrative maps that can support analysts through their sensemaking process.
We find that our SI system can model the analysts' intent and support incremental formalism for narrative maps.
arXiv Detail & Related papers (2023-02-13T15:32:10Z) - What You Say Is What You Show: Visual Narration Detection in
Instructional Videos [108.77600799637172]
We introduce the novel task of visual narration detection, which entails determining whether a narration is visually depicted by the actions in the video.
We propose What You Say is What You Show (WYS2), a method that leverages multi-modal cues and pseudo-labeling to learn to detect visual narrations with only weakly labeled data.
Our model successfully detects visual narrations in in-the-wild videos, outperforming strong baselines, and we demonstrate its impact for state-of-the-art summarization and temporal alignment of instructional videos.
arXiv Detail & Related papers (2023-01-05T21:43:19Z) - Look at What I'm Doing: Self-Supervised Spatial Grounding of Narrations
in Instructional Videos [78.34818195786846]
We introduce the task of spatially localizing narrated interactions in videos.
Key to our approach is the ability to learn to spatially localize interactions with self-supervision on a large corpus of videos with accompanying transcribed narrations.
We propose a multilayer cross-modal attention network that enables effective optimization of a contrastive loss during training.
arXiv Detail & Related papers (2021-10-20T14:45:13Z) - Once Upon A Time In Visualization: Understanding the Use of Textual
Narratives for Causality [21.67542584041709]
Causality visualization can help people understand temporal chains of events.
But as the scale and complexity of these event sequences grows, even these visualizations can become overwhelming to use.
We propose the use of textual narratives as a data-driven storytelling method to augment causality visualization.
arXiv Detail & Related papers (2020-09-06T05:46:24Z) - PlotMachines: Outline-Conditioned Generation with Dynamic Plot State
Tracking [128.76063992147016]
We present PlotMachines, a neural narrative model that learns to transform an outline into a coherent story by tracking the dynamic plot states.
In addition, we enrich PlotMachines with high-level discourse structure so that the model can learn different writing styles corresponding to different parts of the narrative.
arXiv Detail & Related papers (2020-04-30T17:16:31Z) - Screenplay Summarization Using Latent Narrative Structure [78.45316339164133]
We propose to explicitly incorporate the underlying structure of narratives into general unsupervised and supervised extractive summarization models.
We formalize narrative structure in terms of key narrative events (turning points) and treat it as latent in order to summarize screenplays.
Experimental results on the CSI corpus of TV screenplays, which we augment with scene-level summarization labels, show that latent turning points correlate with important aspects of a CSI episode.
arXiv Detail & Related papers (2020-04-27T11:54:19Z) - Tension Space Analysis for Emergent Narrative [0.1784936803975635]
We present a novel approach to emergent narrative using the narratological theory of possible worlds.
We demonstrate how the design of works in such a system can be understood through a formal means of analysis inspired by expressive range analysis.
Finally, we propose a novel way through which content may be authored for the emergent narrative system using a sketch-based interface.
arXiv Detail & Related papers (2020-04-22T19:26:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.