Narrative Science Systems: A Review
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paper_title: Automatic generation of natural language nursing shift summaries in neonatal intensive care: BT-Nurse

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Introduction: Our objective was to determine whether and how a computer system could automatically generate helpful natural language nursing shift summaries solely from an electronic patient record system, in a neonatal intensive care unit (NICU). Methods: A system was developed which automatically generates partial NICU shift summaries (for the respiratory and cardiovascular systems), using data-to-text technology. It was evaluated for 2 months in the NICU at the Royal Infirmary of Edinburgh, under supervision. Results: In an on-ward evaluation, a substantial majority of the summaries was found by outgoing and incoming nurses to be understandable (90%), and a majority was found to be accurate (70%), and helpful (59%). The evaluation also served to identify some outstanding issues, especially with regard to extra content the nurses wanted to see in the computer-generated summaries. Conclusions: It is technically possible automatically to generate limited natural language NICU shift summaries from an electronic patient record. However, it proved difficult to handle electronic data that was intended primarily for display to the medical staff, and considerable engineering effort would be required to create a deployable system from our proof-of-concept software.
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paper_title: Generating Different Story Tellings from Semantic Representations of Narrative

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In order to tell stories in different voices for different audiences, interactive story systems require: (1) a semantic representation of story structure, and (2) the ability to automatically generate story and dialogue from this semantic representation using some form of Natural Language Generation (nlg). However, there has been limited research on methods for linking story structures to narrative descriptions of scenes and story events. In this paper we present an automatic method for converting from Scheherazade's story intention graph, a semantic representation, to the input required by the personage nlg engine. Using 36 Aesop Fables distributed in DramaBank, a collection of story encodings, we train translation rules on one story and then test these rules by generating text for the remaining 35. The results are measured in terms of the string similarity metrics Levenshtein Distance and BLEU score. The results show that we can generate the 35 stories with correct content: the test set stories on average are close to the output of the Scheherazade realizer, which was customized to this semantic representation. We provide some examples of story variations generated by personage. In future work, we will experiment with measuring the quality of the same stories generated in different voices, and with techniques for making storytelling interactive.
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paper_title: A State-Event Transformation Mechanism for Generating Micro Structures of Story in an Integrated Narrative Generation System

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A State-Event Transformation Mechanism for Generating Micro Structures of Story in an Integrated Narrative Generation System Kou Onodera (g231i007@s.iwate-pu.ac.jp) Graduate School of Software and Information Science, Iwate Prefectural University, 152-52 Sugo Takizawa, Iwate 020-0193 Japan Taisuke Akimoto (g236i001@s.iwate-pu.ac.jp) Graduate School of Software and Information Science, Iwate Prefectural University Takashi Ogata (t-ogata@iwate-pu.ac.jp) Faculty of Software and Information Science, Iwate Prefectural University Abstract stories using a conceptual dictionary for noun/verb concepts and transformation rules. We analyze and classify the relationship between an action and the states in front and behind for 689 verb concepts to develop a mechanism that mutually transforms from an action to states or from states to an action and cyclically repeats the process. The action means an event in which a verb concept for an action is included as the central element. A temporal sequence of events is corresponding to a story. On the other hand, a collection of states means a static narrative knowledge supporting events. This paper describes the current version of state-event transformation system to transform story lines and story worlds each other using a knowledge base and a conceptual dictionary. Moreover, we extend the basic framework to a circulative generation mechanism with a simple mutation function. Through the preliminary performance checks, we confirmed that the transformation of story worlds and story lines is approximately logically adequate and the circular process produces the diversity of stories. The proposed system is a module in an integrated narrative generation system and the pilot version is already implemented. In the context of the narrative generation system, the proposed system plays roles for expanding the variation of discourse to be generated and limiting possible narrative elements at any given time in a narraive generation process. Narrative Generation and Proposed Mechanism Keywords: Narrative generation system; story generation; state; action; conceptual dictionary. Introduction In the context of natural language processing in the wide sense, the research of narrative generation system which aims at automatic generation of narrative texts by computer has been developing from 1960s. Meehan (1977) shows a classical approach and Bringsjord and Ferrucci (2000) is an example of the comparatively new result. Along with traditional literary genres, narrative and story will play important roles for digital entertainment genres such as computer game. The mechanism we propose in this paper is a part in an integrated narrative generation system we have studied as a project. The applicable goal is creating novel contents such as automatic generation game, which has not a fixed story, and narrative generation based narrative or literature, which is a form of novel containing narrative generation mechanisms. Moreover, narrative is the strongest method for organizing and structuring fragmentary information and a kind of collective knowledge in human being. The narrative generation system is also associated with a variety of issues such as the organic formation of fragmentary information, the diverse interpretation of an event or events, and so on (Ogata & Kanai, 2010). In this paper, we deal with a part of mechanism for generating a story, which is a sequence of events to be narrated, in the narrative generation process. In concrete terms, we propose a system to make a correlation between state and event (action) which are main elements to construct Three main modules of our narrative generation system are story generation mechanism, discourse mechanism, and surface expression mechanism. Story means a sequence of events to be narrated and discourse means the narrated structure of events, and both are described with conceptual representation. Expression contains surface representations including natural language, animated movie, music, and so on. The system has a conceptual dictionary and various narrative knowledge bases used mainly in story and discourse parts. Although our previous works was to develop the comparatively independent modules, we are currently starting to complete an integrated narrative generation system in which a variety of mechanisms are synthesized by standardizing data structure for event representation and constructing a conceptual dictionary to be used in a lot of modules commonly (Akimoto & Ogata, 2011). Figure 1 shows the overall structure of a pilot version of the system. The proposed system in this paper is correnponding to both “SL (story line)→SW (story world)” and ”SW→SL” in the “Structual operation module”. As mentioned later in detaile, a story line is a sequence of events and a story world is a collection of states. Generative parameters (input by user) Entire control or Control mechanism SL→SW control Story control SW→SL control PROPP control Discourse control CM control Expression control Music NLG mechanism control Movie control Discourse Movie Structural SL→SW SW→SL PROPP CM NLG techniques generation operation mechanisms - Each box is a function Conceptual dictionary - Each arrow means function call (and receive a result) - verb concepts (around 12000) - noun concepts (around 120000) Figure 1: The overall structure of an integrated narrative generation system
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paper_title: Live topic generation from event streams

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Social platforms constantly record streams of heterogeneous data about human's activities, feelings, emotions and conversations opening a window to the world in real-time. Trends can be computed but making sense out of them is an extremely challenging task due to the heterogeneity of the data and its dynamics making often short-lived phenomena. We develop a framework which collects microposts shared on social platforms that contain media items as a result of a query, for example a trending event. It automatically creates different visual storyboards that reflect what users have shared about this particular event. More precisely it leverages on: (i) visual features from media items for near-deduplication, and (ii) textual features from status updates to interpret, cluster, and visualize media items. A screencast showing an example of these functionalities is published at: http://youtu.be/8iRiwz7cDYY while the prototype is publicly available at http://mediafinder.eurecom.fr.
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paper_title: Automatically generating stories from sensor data

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Recent research in Augmented and Alternative Communication (AAC) has begun to make use of Natural Language Generation (NLG) techniques. This creates an opportunity for constructing stories from sensor data, akin to existing work in life-logging. This paper examines the potential of using NLG to merge the AAC and life-logging domains. It proposes a four stage hierarchy that categorises levels of complexity of output text. It formulates a key subproblem of clustering sensor data into narrative events and describes three potential approaches for resolving this subproblem.
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paper_title: Narrative Planning: Balancing Plot and Character

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Narrative, and in particular storytelling, is an important part of the human experience. Consequently, computational systems that can reason about narrative can be more effective communicators, entertainers, educators, and trainers. One of the central challenges in computational narrative reasoning is narrative generation, the automated creation of meaningful event sequences. There are many factors - logical and aesthetic - that contribute to the success of a narrative artifact. Central to this success is its understandability. We argue that the following two attributes of narratives are universal: (a) the logical causal progression of plot, and (b) character believability. Character believability is the perception by the audience that the actions performed by characters do not negatively impact the audience's suspension of disbelief. Specifically, characters must be perceived by the audience to be intentional agents. In this article, we explore the use of refinement search as a technique for solving the narrative generation problem - to find a sound and believable sequence of character actions that transforms an initial world state into a world state in which goal propositions hold. We describe a novel refinement search planning algorithm - the Intent-based Partial Order Causal Link (IPOCL) planner - that, in addition to creating causally sound plot progression, reasons about character intentionality by identifying possible character goals that explain their actions and creating plan structures that explain why those characters commit to their goals. We present the results of an empirical evaluation that demonstrates that narrative plans generated by the IPOCL algorithm support audience comprehension of character intentions better than plans generated by conventional partial-order planners.
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paper_title: Automatically generating stories from sensor data

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Recent research in Augmented and Alternative Communication (AAC) has begun to make use of Natural Language Generation (NLG) techniques. This creates an opportunity for constructing stories from sensor data, akin to existing work in life-logging. This paper examines the potential of using NLG to merge the AAC and life-logging domains. It proposes a four stage hierarchy that categorises levels of complexity of output text. It formulates a key subproblem of clustering sensor data into narrative events and describes three potential approaches for resolving this subproblem.
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paper_title: Automatic generation of video narratives from shared UGC

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This paper introduces an evaluated approach to the automatic generation of video narratives from user generated content gathered in a shared repository. In the context of social events, end-users record video material with their personal cameras and upload the content to a common repository. Video narrative techniques, implemented using Narrative Structure Language (NSL) and ShapeShifting Media, are employed to automatically generate movies recounting the event. Such movies are personalized according to the preferences expressed by each individual end-user, for each individual viewing. This paper describes our prototype narrative system, MyVideos, deployed as a web application, and reports on its evaluation for one specific use case: assembling stories of a school concert by parents, relatives and friends. The evaluations carried out through focus groups, interviews and field trials, in the Netherlands and UK, provided validating results and further insights into this approach.
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paper_title: Database Management System

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A method and a system of database divisional management for use with a parallel database system comprising an FES (front end server), BES's (back end servers), an IOS (I/O server) and disk units. The numbers of processors assigned to the FES, BES's and IOS, the number of disk units, and the number of partitions of the disk units are determined in accordance with the load pattern in question. Illustratively, there may be established a configuration of one FES, four BES's, one IOS and eight disk units. The number of BEST's is varied from one to four depending on the fluctuation in load, so that a scalable system configuration is implemented. When the number of BES's is increased or decreased, only the management information thereabout is transferred between nodes and not the data, whereby the desired degree of parallelism is obtained for high-speed query processing.
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paper_title: MixT: automatic generation of step-by-step mixed media tutorials

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As software interfaces become more complicated, users rely on tutorials to learn, creating an increasing demand for effective tutorials. Existing tutorials, however, are limited in their presentation: Static step-by-step tutorials are easy to scan but hard to create and don't always give all of the necessary information for how to accomplish a step. In contrast, video tutorials provide very detailed information and are easy to create, but they are hard to scan as the video-player timeline does not give an overview of the entire task. We present MixT, which automatically generates mixed media tutorials that combine the strengths of these tutorial types. MixT tutorials include step-by-step text descriptions and images that are easy to scan and short videos for each step that provide additional context and detail as needed. We ground our design in a formative study that shows that mixed-media tutorials outperform both static and video tutorials.
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paper_title: Data Analysis for Massively Distributed Simulations

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More computing power allows increases in the fidelity of simulations. Fast networking allows large clusters of high performance computing resources, often distributed across wide geographic areas, to be brought to bear on the simulations. This increase in fidelity has correspondingly increased the volumes of data simulations are capable of generating. Coordinating distant computing resources and making sense of this mass of data is a problem that must be addressed. Unless data are analyzed and converted into information, simulations will provide no useful knowledge. This paper reports on experiments using distributed analysis, particularly the Apache Hadoop framework, to address the analysis issues and suggests directions for enhancing the analysis capabilities to keep pace with the data generating capabilities found in modern simulation environments. Hadoop provides a scalable, but conceptually simple, distributed computation paradigm based on map/reduce operations implemented over a highly parallel, distributed filesystem. We developed map/reduce implementations of K-Means and ExpectationMaximization data mining algorithms that take advantage of the Hadoop framework. The Hadoop filesystem dramatically improves the disk scan time needed by these iterative data mining algorithms. We ran these algorithms across multiple Linux clusters over specially reserved high speed networks. The results of these experiments point to potential enhancements for Hadoop and other analysis tools.
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paper_title: Introduction to Artificial Intelligence

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This book is an introduction on artificial intelligence. Topics include reasoning under uncertainty, robot plans, language understanding, and learning. The history of the field as well as intellectual ties to related disciplines are presented.
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Title: Narrative Science Systems: A Review

Section 1: INTRODUCTION
Description 1: Introduce the importance of data and the potential of systems that can generate narratives from data.

Section 2: RELATED WORK
Description 2: Review significant research and methods in the field of narrative science systems, highlighting key contributions and approaches.

Section 3: ARCHITECTURE OF NARRATION GENERATION SYSTEM
Description 3: Describe the components and processes involved in building an automatic narration generation system.

Section 4: DISCUSSION
Description 4: Discuss the limitations, challenges, and potential improvements in narrative science systems, focusing on content quality and evaluation methods.

Section 5: CONCLUSION
Description 5: Summarize the study's findings and suggest future research directions for improving automatic narration generation systems.