Internationalof Computational
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Robotics,
Networking andArtificial Life
Vol.
XX(Z);
Month(2018),(Year), pp.
pp. 212–216
xx–yy
Vol.
5(3);
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DOI:
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onlineDOI:
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Research Article

Acquiring Short Scripts and Setting a Case Frame in Each
Acquired Script: Toward Random Story Generation
Jumpei Ono1,*, Takashi Ogata2
Department of IT Solution, Vocational School of Digital Arts Sendai, Sendai, Miyagi 980-0003, Japan
Department of IT Solution, Faculty of Software and Information Science, Iwate Prefectural University, Takizawa, Iwate 020-0693, Japan

12ARTICLE INFO
Article History
Received 3 June 2018
Accepted 30 September 2018
Keywords
Case frame
integrated narrative generation
systemknowledge acquisition
random story generation
scriptverb conceptABSTRACT
The integrated narrative generation system (INGS), that the authors have developed, generates a story to translate the story into
the surface representation. In the story generation process, the integrated narrative generation system uses narrative knowledge
that was automatically acquired from existing narrative works. This paper presents a method to acquire short scripts, which area kind of narrative knowledge, from existing works in Aozora Bunko for the story generation. This paper presents a mechanism
to generate random story-like event sequences by using 23,751,142 bi-gram scripts acquired based on the method proposed
below. The authors aim to use the scripts generated by the method as a first set to be revised through the next learning process.

© 2018 The Authors. Published by Atlantis Press SARL.
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
The integrated narrative generation system (INGS) [1] is a systemthat generates a narrative automatically. The system not only generates a story but translates the story into the surface representation.
In story generation, the INGS uses knowledge about story structure and dictionaries [2] that systematically store nouns, verbs, andso on. The authors call story content knowledge, which is stored
knowledge bases of the INGS. The dictionaries are called conceptual dictionaries. A variety of generated stories depend on the scaleof the knowledge base. If the knowledge base is small scale, story
generation by the INGS fails frequently. However, expanding theknowledge base manually has limitations. Therefore, the authors
have attempted to acquire knowledge automatically [3]. This
paper details the attempt to acquire short scripts based on bigrams
acquired from existing narrative works for story generation.
A script that Schank [4] has mentioned involves knowledge abouthuman actions in a particular situation. For example, a restaurant
script describes the procedure procedures for ordering and eating
dinner at a restaurant. We attempt to not only acquire Schank’s
scripts but to also acquire the temporal sequences of events.
The INGS consists of one-part mechanisms and one-part knowledge. Knowledge refers to dictionaries for concepts and language
notation [1], and to knowledge bases that build partial structuresof a story (e.g., the narrative content knowledge base). By usingthis knowledge, the mechanisms generate various aspects in anarrative, such as a story, discourse, and surface representation(i.e., sentences, music, or images).
* Corresponding author. Email: j.ono@sugawara.ac.jp

2. METHOD FOR STORY GENERATION
IN THE INGS
Figure 1 shows a story structure that is generated in the INGS. Thestructure has a hierarchical structure that consists of states, events,and relations. A state describes static information regarding characters, things, locations, and times in a story. An event describes
dynamic information that represents the difference between twostates. A relation links several events by some relationship such as
Causal relationship.
Story generation in the INGS is the expansion and transformationof a story structure using story techniques. A story technique is aformal procedure for expanding the structure of a story and draws
on a knowledge base that corresponds to the particular technique.
Figure 2 shows an example of using a story technique; the technique selects applicable knowledge and expands the structure of
the story by using that knowledge.
Figure 3 shows an example of automatically-acquired knowledge.
This knowledge is applied to an event that includes “eat,” and thestory technique adds an event that includes “drink” to the structure of the story. (&v age1) or (&v obj1) are variables describedby the cases. These variables denote the position or location where
the generated character, thing, or location is inserted based on therestrictions. Cases that have the same variables also have the samecharacter, thing, or location.
Potential selection points in story generation involve mainly the“application point of the story technique,” “applying a story technique,” and “using knowledge.” The “application point of storytechnique” and “applying a story technique” steps can be controlled

J. Ono and T. Ogata / Journal of Robotics, Networking and Artificial Life 5(3) 212–216

213(i) Morphological analysis: The mechanism analyzes a text using
a morphological analyzer.
(ii) Extracting verbs from results: The mechanism extracts verbs
from the results of morphological analysis.
(iii) Making bigrams: The mechanism creates pairs of verbs in theorder of the results of extraction. For example, if the result ofextraction is (A × B × C), then the mechanism combines twopairs of verbs (A × B) and (B × C). N − 1 verb bigrams are created from N verbs.

Figure 1 | An example of a story structure(iv) Removing a part of the bigram: The mechanism removes bigrams
that include verbs that are not stored in the conceptual dictionary,because the INGS cannot use such bigrams in story generation.

3.2. Making Short Scripts
The acquired verb bigrams are made into short scripts. This step
connects the verb concepts to verbs that are included in verbbigrams. Verb concepts are assigned a number in the INGS because
the INGS provides the plural of the verb. For example, the verb
concept “食べる1” has a meaning like “earn,” while the verb concept “食べる2” has a meaning like “eat”.

Figure 2 | A method for expanding a story structure (Figure 8 in
Ogata [1])Short scripts (Xn × Yk) were created from verb bigram (X × Y)[(Xn × Yk) consists of verb concepts Xn and Yk; (X × Y) consists ofverb X and verb Y]. In this case, verb Xn has i kinds of meaning,verb Yk has j kinds of meaning, and the number of short scripts isequal to i multiplied by j. In making short scripts, we need to consider the meaning of a verb. However, in this paper, the authors created all patterns of (Xn × Yk) with the procedure shown in Figure 5.

3.3. Setting a Case Frame
By setting a case frame in a short script, the INGS can generate
a story using the short script. Figure 6 shows the procedure forFigure 3 | Example of a short script

based on a parameter that is inputted into the INGS. For example, if “parameter length” is increased, a story technique is selectedthat increases the number of events within the story.

3. ACQUIRING SHORT SCRIPTS FROMEXISTING WORKS
The mechanism for acquiring short scripts has three steps: acquiring bigrams, making short scripts, and setting a case frame. Thissection explains each of the three steps.

Figure 4 | Example of acquired bigrams

3.1. Acquiring Bigrams from Existing Text
Bigrams that consist of two verbs are acquired by applying morphological analysis to existing text. The acquired bigram is called averb bigram. For example, verb bigrams are acquired as shown inFigure 4. The procedure for acquiring bigrams is as follows:Figure 5 | An image of making short scripts by a bigram

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setting the case frame in short scripts (Xn × Yk). A case frame consists of cases that require a verb. For example, a verb concept has
a description as seen in Figure 7. Here, the elements of the “caseframe” are the case frame. A case frame has restrictions based on
the noun conceptual dictionary. The restrictions define the probability of cases that are included in a verb concept.

4. RESULTIn this paper, the authors acquired short scripts from 13,331 works
in the Aozora Bunko [5]. The acquisition mechanism used MeCab in
the morphological analysis. We acquired short scripts from eachwork. In analyzing each work, we did not consider structures suchas paragraphs in each work. Table 1 shows the number of acquired
verb bigrams and short scripts (MeCab was downloaded from“http://taku910.github.io/mecab/”).

5. USING SHORT SCRIPTS FOR STORY
GENERATIONThe INGS uses a technique that allows the generated story to converge
on one result based on several parameters that are set beforehand.
Specifically, the INGS performs story generation by using selection
rules at several points of the generation process. The authors haveadopted random generation in order to gradually change a narrative.
A summary regarding random narrative generation is presented inOgata et al. [6]. In this section, a generated story is presented.

Figure 6 | A flowchart for setting a case frame

Currently, parameters have not been applied by the authors inorder to evaluate whether acquired knowledge alone suffices for
random story generation. This method accomplishes generation
by applying various story techniques that INGS has available, anduses scripts that were acquired automatically which are stored

Figure 7 | Example of a verb concept

J. Ono and T. Ogata / Journal of Robotics, Networking and Artificial Life 5(3) 212–216

Table 1 | Amount of short scripts
TypeAmount

Verb bigram
Short script

5,52,773
23,751,142

215

how a generated story is transformed into a natural language sentence by a sentence generation mechanism in the INGS. Although
the authors have experience with the topic of random generation,
previous attempts to generate a story through random methods
failed owing to the lack of a knowledge base.

6. CONCLUSION
This paper showed short script acquisition from existing works.
The authors acquired 23,751,142 short scripts from existing works
that were stored the Aozora Bunko. The acquired short scripts wereused for story generation in the INGS. In future works, the authors
will acquire longer scripts. In addition, they will attempt to acquire
scripts semantically, because the results in this paper did not consider the semantic structure of a story.
The authors’ goal is the random generation of a story. We presented
story generation by using knowledge, which was obtained automatically. Based on the discussion above, a method to change narratives
and to set parameters for a narrative generation was presented.
For future research, the aim is to increase automation by using
learning mechanisms. For example, we propose a generation strategy composed of a preliminary story generation, a valuation of thestory, and elements conjecture where operating parameters are not
input manually, and instead are selected automatically. Throughthe learning operations at some selection points, generation results
improve using a bottom-up process. A second option would be theconstruction of a system although maintaining the consistency of astory in a narrative generation process is very difficult, and maintaining consistency would be our goal.

REFERENCES

Figure 8 | Generated sentence using a story structure

hierarchically, based on verbs. When the INGS selects an event that
randomly, and if the event can be expanded by any story technique,the INGS selects a story technique in those story techniques randomly. However, if the event cannot be expanded by any story technique, the INGS then selects another event randomly. Figure 8 shows[1] T. Ogata, Computational and cognitive approaches to narratology
from the perspective of narrative generation, in: T. Ogata, T.
Akimoto (Eds.), Computational and Cognitive Approaches toNarratology, IGI Global, Pennsylvania, 2016, pp. 1–74.
[2] T. Ogata, Building conceptual dictionaries for an integrated
narrative generation system, J. Robot. Netw. Artif. Life 1 (2015),270–284.
[3] T. Arai, T. Ogata, Acquiring and using event sequences based onverbs from novels, Proceedings of the 31st Annual Conference of theJapanese Society for Artificial Intelligence, 2017, 1D2-OS-29a-5in2.
[4] R.C. Schank, Tell Me a Story: A New Look at Real and ArtificialMemory, John Brockman Associates, New York, 1990.
[5] Aozora-Bunko, Available from: http://www.aozora.gr.jp, 1998
(accessed December 2016).
[6] T. Ogata, T. Arai, J. Ono, Random Narrative Generation in anIntegrated Narrative Generation System, Proceedings of theForum on Information Technology, Information Processing
Society of Japan, 2016, pp. 133–134.

J. Ono and T. Ogata / Journal of Robotics, Networking and Artificial Life 5(3) 212–216

216Authors Introduction
Dr. Jumpei Ono

Dr. Takashi Ogata
He received his Ph.D. from Iwate
Prefectural University, Japan in 2018.
He is now working as an instructor in
Vocational School of Digital Arts Sendai
in Japan. His research interest includes
artificial intelligence, cognitive science,natural language processing, narratology,narrative generation, and game.

He received his Ph.D. in the University ofTokyo in 1995. He has industrial experience since 1983 at software development
companies. He is a Professor of the Facultyof Software and Information Science at
Iwate Prefectural University since 2005.
His major research interests include artificial intelligence, cognitive science,natural language processing, narratology and literary theories,
an interdisciplinary approach to the development of narrative
generation systems based on AI and narratology, and the application to narrative creation and business.