what is the story? transforming news events into narratives

What is the story?Transforming News Events into Narratives

Frank Smit5887429

Bachelor thesisCredits: 9 EC

Bachelor Opleiding Kunstmatige Intelligentie

University of AmsterdamFaculty of ScienceScience Park 904

1098 XH Amsterdam

SupervisorDr. F.M. Nack

Information and Language Processing SystemsFaculty of Science

University of AmsterdamScience Park 904

1098 XH Amsterdam

June 24th, 2011

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Abstract

Using real world events as an inspiration source for writing a bookor screenplay is done by many authors. We aim to establish a story en-gine that describes an automated process to transform news events intonarrative events, which will help answer our research question: how canwe transform ‘real world events’ to ‘narrative events’? The transfor-mation is done by thematic planning, this means that the theme isresponsible for the type of plan created. The result of the engine is abasic story skeleton which could be further developed by an author tocreate a full story. The resulted story skeleton is evaluated to deter-mine if the generated story skeleton is understood by readers.

Keywords: Automated storytelling, Computational narrative, Events, The-matic planning

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Contents

1 Introduction 4

2 Related Work 4

3 Story engine 63.1 Basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.2 Real world event . . . . . . . . . . . . . . . . . . . . . . . . . 63.3 Generation of narrative events . . . . . . . . . . . . . . . . . . 8

3.3.1 Narrative events . . . . . . . . . . . . . . . . . . . . . 83.3.2 Relational Knowledge . . . . . . . . . . . . . . . . . . 83.3.3 World Knowledge . . . . . . . . . . . . . . . . . . . . . 83.3.4 Situational Knowledge . . . . . . . . . . . . . . . . . . 93.3.5 Thematic planning . . . . . . . . . . . . . . . . . . . . 10

3.4 Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4 Evaluation 124.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124.2 Set-up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134.3 Results and evaluation of the results . . . . . . . . . . . . . . 13

5 Conclusion 14

6 Future work 15

References 16

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1 Introduction

Using real world events as an inspiration source for writing a book or screen-play is done by many authors. One of the most famous examples is TrumanCapote’s book In Cold Blood: A True Account of a Multiple Murder and ItsConsequences, Capote (1966), which was inspired by a 300-word article thatran on page 39 of The New York Times on November 16, 1959, reportingabout the unexplained murder of a wealthy farmer in rural Kansas. Similarapproaches can be found in other media than literature, such as film, whereSchindler’s List and Titanic are examples of using real world events as thebasis for works of art. They are works of art as the described events do notnecessarily represent the real happenings, neither in form nor in order.

This transformation from real events into their narrative interpretationis a typical human creative process. It took Capote, for example, 4 years tofinish the book. Even though the fine structured balance of events, humanemotion and argumentative structure is still difficult to be achieved by amachine, simply because the relevant world knowledge and writing expertiseis not available. It seems plausible to allow a machine to scan news articlesand suggest potential ‘narrative material’. In this work we suggest a smallstep in that direction by aiming to establish a framework that describes anautomated process to transform news events into narrative events. We aimfor an engine that does not generate a full-fledged story but rather a story-like skeleton which will be further developed as a real story by an author.In that way we see our approach as a Lego brick in a larger environmentthat can support authors in finding material sources to work with. Forestablishing the basic framework for such an author support tool, we focuson the following research question: how can we transform ‘real world events’to ‘narrative events’? To narrow this down we will only consider the eventconcept of a ‘revolution’.

In this paper, we first outline related work. We then describe the storyengine that was developed to achieve the transformation of news into nar-rative. We then provide the evaluation of a short experiment that shoulddetermine if the generated story skeleton is understood by readers. Weconclude the report with a summary of the work and indicate future work.

2 Related Work

The telling of stories helps us to shape our experience by structuring theevents during our encounters with reality. Narrating means making a com-ment about a certain event, following an idea about the medium and form ofpresentation which is grounded in one’s own motivational and psychologicalattributes. For all of these reasons understanding formed from the early be-ginnings of Artificial intelligence (AI) an important field of research. Based

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on the understanding that events, characters, actions form the core partsof a narrative, it seemed suitable to characterize a plot in terms of statesand transformational rules, an idea which was propagated by the struc-turalist movement. Structuralism formulated a rationalized and deductiveapproach to narration, which considered narrative structure as analogous tolanguage structure and thus linked structure with the determination of con-tent (Chomsky (1965); Propp (1968); Dijk (1972)). An analogous approachto the representation of narrative structure, in the field of AI, consideredthe applicability of story grammars to text understanding, where the maininfluences came from Propp’s work on Russian folktales and Chomsky’stransformational grammar (see Kintsch and Dijk (1978); Rumelhart (1975);Rumelhart (1977)). The main arguments against this approach are advancedby Black and Wilensky (1979). They show that not only are the formal prop-erties of the grammars insufficient, but also that the computational costs ofthe representation is too high. The number of deletion and reordering trans-formations in the proposed grammars become extremely large, and yet thegrammars are unable to produce a sufficiently varied set of stories.

A different approach to story representation and generation was pro-posed during the second half of the 1990ties, proposing that a story is amental process based on different aspects of people’s knowledge, of whichstructure is but one. Nack (1996) describes how systems can generate visualslapstick sequences. An important aspect of this work was the introductionof theme and genre patterns to establish this type of joke. It describes theessential narrative concepts fabula (the potential story space) and the plot(the path through the fabula and how it should be presented) and shows whythe predominant AI approach towards narratively, i.e. grammars, should bereplaced by a planning approach (making use of Schank’s case-based reason-ing technique). A similar view is taken by Brooks (1996), though he appliesan agent rather than planning approach. In his work he postulates thatthere are three components of automated story telling; (1) the structure ofthe story, (2) the collection and organization of story pieces with some rep-resentation of their meaning, and (3) a navigational strategy through thatcollection of story pieces, with style and purpose. Based on these 3 compo-nents he designed Story-Agent system, where each agent could tell stories ina different manner. In addition to this theory Hargood, Millard, and Weal(2010) suggested to add a theme to the process of automated storytelling,this way the thematic cohesion in the systems will improve. They also statedthat there are two approaches in telling a story, (1) character centric and(2) author centric. A thematic model was created which consist of themesand motifs.

Related to the semantic web and the development of experiential sys-tems we have also seen new approaches to define events. These schemataoriented forms of representations aim at the formal description of real worldevents, where the schemata should provide generalized forms to be applied

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for various domains.Besides the possible solution with actions and characters, as mentioned

above, particular emphasis has been put recently on the representation ofevents. The term ‘event’ could be interpret in several different ways. Accord-ing to Shaw, Troncy, and Hardman (2009) it could mean both phenomenathat have happened in the past, or phenomena that did not happened yetbut are scheduled to do so. They used the former form to create a model forpublishing records of events as Linked Data. These events could be charac-terized using an approach suggested by Troncy, Malocha, and Fialho (2010).The suggested to use the four W’s. The four W’s stand for: ‘What hap-pened, Where did it happen, When did it happen, and Who was involved(Troncy et al., 2010, p. 1-2)’.

3 Story engine

3.1 Basics

The transformation from real world events to narrative events is done by astory engine, the basic idea of this engine is shown by figure 1. As shown byfigure 1, this transformation is done by theme. This means that the themeis responsible for the development of the story. The theme is used in theform of a planning model, this means that a plan is created which plans thedevelopment of the story. The thematic planner, uses three different typesof knowledge to create the story skeleton.

The story skeleton created by the engine uses the character centric ap-proach proposed by Hargood et al. (2010), this is done by means of theprotagonist.

Figure 1: Basic idea of the story engine

3.2 Real world event

As described above the basic idea is to transform real world events intonarrative events by using a theme as planning part. In this section we firstdescribe what the input is we are using for the real world events and thenhow we characterize those real world events.

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Characters:

Zine al-Abidine Ben Ali

Hosni Mubarak

Omar Suleiman

people

This is the real story:

Zine al-Abidine Ben Ali resigns as president of tunesia.

1000 of people start demonstrating on the Tahrir Square in egypt.


26 people died.

Omar Suleiman named vice-president of egypt.

Hosni Mubarak sends out the army.


The army calls to stop the protests.

Clashes between the protagonist and antagonist.

Clashes between the protagonist and antagonist.


Hosni Mubarak tells he stays.

Hosni Mubarak resigns as president of egypt.

10000 of people are celebrating on the Tahrir Square in egypt.

Figure 2: Real world events

For the input of the real world events we are using news-articles. Newsarticles are basically a description of real world events that happened inthe past, this fits perfectly the first definition of an event given by Shawet al. (2009). A special case of the news-articles are time-lines of the keymoments in some happening, for example a revolution. These time-lines arevery strict and to the point and therefor only describe the real key moment.For this work we are using a time-line concerning the revolution in Egypt1 asan input source for describing the real world events. The event descriptionfor the engine are at this point annotated by hand.

The four W’s characterizations are enough to describe an event (Troncyet al., 2010). However to make the engine more reactive, we introducea number of extra characterizations. Such additions are, a unique eventidentifier (e1, e2, ...., en), How many participants, and what the Role of theparticipant in the society is, the use of these extra characterizations willbe explained later. The event characterization Where is split into two sub-characterizations, the place and the country. The output of the descriptionfrom the BBC time-line1 into the real world events is shown by figure 2.

1http://www.bbc.co.uk/news/world-middle-east-12425375

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When doing the transformation, the engine takes from the real worldevents; (1) the characters (Who and the characters’ Role), (2) the location(Where, both place and country), and (3) the actions (What). The charac-terization When is not used by the engine, however could be of great use inthe future, see future work. The characterization How many is used by theengine to control the actions that can be taken, this will also be explainedin more detail later.

3.3 Generation of narrative events

3.3.1 Narrative events

The transformation, done by the engine, results in a narrative event. Thenarrative event consist of two parts; (1) a thought of the protagonist, and(2) an action for the protagonist. These thoughts are described by means ofthe protagonists’ Role, the protagonists’ mood, and the real world event thathad triggered the thought. An example of a thought would be a thoughtof the president Mubarak about the resignation of the president of TunisiaZine al-Abidine Ben Ali; that will not happen to me!

The actions that the protagonist could take are described in more detailin the next section.

3.3.2 Relational Knowledge

In situations involving people, relations exist among those peoples. Theserelations could be love relations, power relations, etc. In our revolutionexample there is always one who has the power over the rest, because heis the president. In this case we describe this as the power relation. Forthe engine we are, at the moment, only using the power relation betweenthe characters. These relations determine what type of actions a charactercould take, which will be further explained below.

In our engine we make four power over relations explicit. First; the pres-ident has power over the people. Second; the president has power over thearmy, third; the army has power over the people, and fourth: the presidenthas power over the vice-president. These relations work in one way, thismeans that if the president has power over the people then the people donot have power over the president. More relations could been made explicitbut this is not necessary for the story we would like to create.

3.3.3 World Knowledge

One type of knowledge we can distinguish for the engine is world knowl-edge. This type of knowledge is a description for the engine of conceptsand actions.Concepts are; president, army, folk, revolution, and the actionsare; send army, demonstrate, name vice-president and resign. The actions

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are described by Role of the character. This means that, for example, apresident could send the army out but the people could not. Part of thesedescriptions involve the relational knowledge, which was explained above.

The concepts; president, army, people and vice-president are describedby means of the relational knowledge and the actions they could take. Forexample, the description of a president would be; a president has power overthe vice-president, the army, and the people. A president could send thearmy, name someone vice-president or resign. The last concept ‘revolution’could be described as (Wikipedia, 2011):

1. the people do not agree with the current government

2. The people starts demonstrations

3. A change of power takes place

4. The government resigns

If all the parts of a revolution succeed then the revolution has took place,otherwise we could not call it a revolution but more a revolt.

3.3.4 Situational Knowledge

The last type of knowledge we describe for the engine is situational knowl-edge. This type of knowledge is a description for situations. Every situationhas a consequence for the protagonist or the antagonist of the story. Thismeans that every description of a situation involves changes in the mood ofthe characters. This mood change could trigger a reaction of the protag-onist, which could mean any type of action (e.g. a thought or an actiondescribed by the world knowledge).

For the engine we have created a number of situations. Below are the de-scriptions of every situation, how they control; thoughts, actions and moods.

Trigger This situation can be seen as the trigger that triggers the rest ofthe story. An example can be seen in a revolution, there is alwayssome event that triggers the revolution. In case of the revolution inEgypt this was the event that the president of Tunisia resigned. Thissituation results in a thought of the protagonist.

Conflict The conflict situation is always the same in case of a revolutionstory; there is a leader and the people want the leader to resign, how-ever the leader does not want to resign.

Disagreement In a disagreement there is one giver and a receiver, in thecase of a demonstration (which we call a disagreement) the people arethe givers and the president is the receiver. The effect for the presidentdepends on the number of demonstrators. As explained above in the

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world knowledge, the number of demonstrators controls which actionsor thoughts the protagonist could have.

We distinguish tree types of demonstrations, (1) small demonstra-tion (a demonstration with no more than a 1000 people), (2) mediumdemonstration (between 1000 and 10.000 people), and a big demon-stration (more than 100.000 people). The actions, mood changes andthoughts are different for every situation. For the first one there ischange in mood and no action, for the second one the protagonistwant to do something to stop the demonstrations so he could send thearmy (which is an action), and for the last one there is a mood changefor the protagonist; he becomes more insecure.

Turning point This situation means that the leader decides to name asecond leader to calm down the people. In the case of the revolutionin Egypt, Mubarak names Suleiman as vice-president.

Change power The leader decides to resign, which means that the secondleader becomes the new leader. This results in an action for the people,they reached their goal and start celebrating. This situation also resultin a change of the president’s mood, he becomes evil (like an evilmastermind).

Old leader controls new leader This is the situation where the old leadermakes clear that he still has control over the new leader.

3.3.5 Thematic planning

Until now we have discussed, how the real world events are described andwhat we need from them (action, location and characters), which type ofknowledge the engine has and how we have described this knowledge, andwhat the narrative events look like but we still have no way of transformingthe real world events into narrative events (thoughts and actions).

An method proposed by Nack (1996) is to use a planner to transformthe events. To extend this, we would like to do this planning by means of atheme. In our definition, a thematic planner uses a plan to walk trough thesituation described by the situational knowledge. For this work we create afixed plan, to use by the thematic planner in order to create the narrativeevents. This plan is controlled by a theme, which is in our case power wins.In this plan we predefine who the protagonist is ans who the antagonist. Theplan is presented as a flow chart shown by figure 3. In this flowchart therhombuses represent the situational knowledge.

As shown by figure 3, the situational knowledge takes the informationneeded from the real world event (action, location and characters) andcreates thoughts, actions (which represent the narrative event) and moodchanges, in the manner described in the section 3.3.4.

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It is possible to create multiple plans for the engine, an example of a planfor the theme love wins would be a plan where; a boy meets a girl, fallsin love with the girl, the girls choses not for the boy but for her work, girlchanges her mind, boy and girl getting married, they lived happily ever after.However another theme means that other situations need to be describedfor the engine.

Figure 3: Flowchart of the transformation with theme: power wins

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3.4 Output

As discussed earlier, the engine is intended to be used as an author supporttool. Therefor the output of the engine is only a story skeleton of thoughts,events and actions. After that it is up to the author to fill in the rest ofthe story as he would prefer. The result of the engine, based on the input(discussed in section 3.2) and the theme power wins (where the protagonistis ‘Hosni Mubarak’ and the antagonist ‘people’), is given by figure 4.

1.Zine al-Abidine Ben Ali resigns as president of tunesia.

2.Hosni Mubarak: That will not happen to me!

3.people wants Mubarak to resign.

4.1000 of people start demonstrating on the Tahrir Square in egypt.

5.Hosni Mubarak: Let them demonstrate, it is their right.


7.Hosni Mubarak: They will not get away with this!

8.Hosni Mubarak sends out the army.

9.Hosni Mubarak: That will teach them!


11. Hosni Mubarak: Maybe I should listen to the people?!

12. Hosni Mubarak: Let me introduce you to a close friend!

13.Omar Suleiman named vice-president of egypt.

14.Hosni Mubarak resigns as president of egypt.

15.10000 of people are celebrating on the egypt in Tahrir Square.

16.Hosni Mubarak: Haha, stupid people!

17.Converstation between Hosni Mubarak and Omar Suleiman

18.Hosni Mubarak: Punish the people! Let them know who is in charge.

Figure 4: Story skeleton generated by the engine

4 Evaluation

4.1 Motivation

At this point it is clear that the engine is capable of outputting a basicstory skeleton made from real world events. To do this we used a thematicapproach, where the theme is implicitly shown in the story skeleton. Becausewe use a theme we would like to do a little experiment which determines ifthe generated story skeleton is understood by readers, in other words if thetheme is clear by the readers. What we also would like to see is that the useof the characters’ mood is clear to the user, that they could spot where themood is changed.

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4.2 Set-up

The experiment consist of 5 participants, 3 participants are student at VrijeUniversiteit (VU) in Amsterdam and 2 participants are student at the Uni-versiteit van Amsterdam (UvA). The 5 participants are between the ages of18 and 26. The participants were all handed an evaluation form with a shortintroduction, which introduced the research and explained the context, thestory skeleton (as shown by figure 4) and the question. These questions areas follows:Question 1Which of the following three themes is the theme of this story skeleton?

1. Power wins theme

2. Love wins theme

3. Corruption theme

Question 2In the story, Mubarak undergoes two mood changes. The story starts withMubarak feeling very powerful, then he becomes insecure about his currentposition (president) and at the end he becomes evil. Could you point outwhere these transitions happen (before which line)?

1. Insecure before line:

2. Evil before line:

The correct answer to question 1 would of course be the power wins themeand the correct answers to the subquestions of the second question are,insecure before line 11 and evil before line 16.

The participants were ask to read the form and fill in the questions,within a timespan of 5 minutes. The results of this questionnaire is shownin the next subsection.

4.3 Results and evaluation of the results

The results of the questionnaire are shown in figure 5 and 6. Three out of the5 participants thought that the theme of the story skeleton was corruption,this was a surprising result. However after asking the participants whatthere idea was behind choosing the corruption theme, it became clear thatall the three participants thought until line 15 that it was a power winstheme. After reading line 16 they thought that it went more in the way ofthe corruption theme. They all explained that if in the story skeleton HosniMubarak did not want to punish the people but showed in another way thathe had still the power over the new president Omar Suleiman, it should bemore clear that the theme of the story was power wins.

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Figure 5: Results in number of votes on question 1

The answers to question 2 resulted as expected (figure 6). The figureshows that it was clear for the participants where the mood started to changefor Mubarak.

Figure 6: Results in number of votes on question 2

5 Conclusion

In this paper, we have created an engine which is able to transform real worldevents into narrative events. This transformation was done by thematicplanning, this means that the theme is responsible for the planning of thedevelopment of the story. The engine was built in order to answer theresearch question: how can we transform ‘real world events’ to ‘narrativeevents’? The way we have done the transformation, by thematic planning,is one of the possible solutions. Also the approach of Brooks (1996) couldbe used as a solution, instead of creating a plan we would use Story-Agents.

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The engine results into a story skeleton which could be further developedby an author. The story skeleton was evaluated by means of a questionnaire.The theme was somehow clear to the reader, however the output of theengine needs a little tweaking. The mood changes of Mubarak were clear inthe story skeleton, we could state that these mood changes could help anauthor to further develop the story.

The use of a theme to plan the development of the story has as an ad-vantage that it is easy to implement and the development of the story iseasy to track. The disadvantage of this use is that (1) a new planning strat-egy needs to be implemented for every different theme, and (2) combiningdifferent themes are harder this way then when using a approach from, forexample, Brooks (1996).

6 Future work

The engine could be extended in a couple of ways. First of all with us-ing the When characterization for events. The dates could be used by areasoning system which could create interval relations described by Allen(1991). These interval relations then could be used to tell the story. Anexample would be if two events have the equal interval relation, then theengine could generate a story which says; event e1 is happening, meanwhileevent e2 takes place. Also for this reason we added a unique identifier tothe event description. This way the engine could create a more coherentstoryline.

After the engine outputted the story skeleton it could support the authorwith the task of creating the story, by suggesting some story material to theauthor. This could be done by attaching the real news-articles to the eventdescription. What then happens is that if the narrative event was generated,the news-article is passed along to the narrative event. The author then doesnot only receives the narrative event from the engine, but also the news-article. If the news-articles were also formatted in a way that they couldimmediately be used by the engine, we could use the system developed byBrooks (1996) and use the news-articles as story blocks.

At this point time-lines are used to fill the event descriptions, but whatif there does not exist such a time-line for some topic. An extension to theengine would be to use a topic clustering method to search for news-articleswith the same topic. This approach can be based on the work of Ide etal. (2010). The basic idea is to segment the news-articles into stories, dotopic extraction on every news-article, cluster the topics, and extract theclusters. This way no time-line is needed and most of the story engine couldbe automated.

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References

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Black, J. B., & Wilensky, R. (1979). An evaluation of story grammars.Cognitive Science, 3 , 213-230.

Brooks, K. M. (1996). Do story agents use rocking chairs? the theoryand implementation of one model for computational narrative. InProceedings of the fourth acm international conference on multimedia(p. 317-328). New York, NY: ACM.

Capote, T. (1966). In cold blood: A true account of a multiple murder andits consequences (First ed.). New York, NY: Random House.

Chomsky, N. (1965). Aspects of the theory of syntax (First ed.). Cambridge,MA: MIT Press.

Dijk, T. A. V. (1972). Some aspects of text grammars : A study in theoreticallinguistics and poetics (First ed.). The Hague: Mouton.

Hargood, C., Millard, D. E., & Weal, M. J. (2010). A semiotic approach forthe generation of themed photo narratives. In Proceedings of the 21stacm conference on hypertext and hypermedia (p. 19-28). New York,NY: ACM.

Ide, I., Kinoshita, T., Mo, T. T. H., Katayama, N., Satoh, S., & Murase, H.(2010). Exploiting the chronological semantic structure in a large-scalebroadcast news video archive for its efficient exploration. In Proceed-ings of the second apsipa annual summit and conference (p. 996-1005).Biopolis, Singapore: APSIPA.

Kintsch, W., & Dijk, T. A. van. (1978). Toward a model of text compre-hension and production. Psychological Review , 85 (5), 363 - 394.

Nack, F. M. (1996). Auteur: The application of video semantics and themerepresentation for automated film editing. Unpublished doctoral dis-sertation.

Propp, V. Y. (1968). Morphology of the folktale (Second ed.). Austin, TX:University of Texas Press.

Rumelhart, D. E. (1975). Notes on a schema for stories. In D. G. Bobrow &A. M. Collins (Eds.), Representation and understanding (p. 211-236).New York, NY: Academic Press, Inc.

Rumelhart, D. E. (1977). Understanding and summarizing brief stories.In D. Laberge & S. J. Samuels (Eds.), Basic processes in reading:Perception and comprehension (p. 265 - 303). Hillsdale, NJ: LawrenceErlbaum Associates.

Shaw, R., Troncy, R., & Hardman, L. (2009). Lode: Linking open de-scriptions of events. In Proceedings of the 4th asian conference on thesemantic web (p. 153-167). Berlin, Heidelberg: Springer-Verlag.

Troncy, R., Malocha, B., & Fialho, A. T. S. (2010). Linking events with

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what is the story? transforming news events into narratives

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