USING NARRATIVES IN COLLABORATIVE WRITING

An Example

Nishadi De Silva

School of Electronics and Computer Science, University of Southampton, SO17 1BJ, UK

Keywords: Collaborative writing, Coherence, Narrative-based writing, Document narratives, Rhetorical Structure

Theory (RST).

Abstract: Document coherence is often harder to achieve in collaborative writing owing to a lack of group consensus

and misaligned contributions by the co-authors. By ‘coherence’ we refer to the feature of a text that makes it

easy to read and understand. This can be linked to the implicit story that a document conveys to its reader.

Despite being an integral aspect of a successful document, software support for coherence is minimal.

Collaborative writing tools do ensure syntactic consistency but this still does not guarantee coherence. Other

approaches such as agreeing on an outline at the start can improve the document but outlines too have their

shortcomings. Previously, we introduced a technique called narrative-based writing to fill these gaps and

built a prototype of a tool that allows co-authors to engage in this method. The purpose of this paper is to

present an example of how a team of authors can make use of this narrative-based technique and tool, and

show how the corresponding document evolves.

1 INTRODUCTION

Collaborative writing (CW) is a necessity in today’s

academic and industrial settings. It is the process in

which multiple authors work together to produce a

single document. It is not just the sharing of

opinions but also the contribution of the various

sections which are collated to form the final

document.

CW has several advantages over single-author

writing. In a survey by Noël and Robert (2004), the

participants agreed that CW resulted in richer

documents owing to diverse ideas and input from

co-authors with different expertise. In theory, CW

should also take less time since the authors produce

the text simultaneously. Also, if each section is

written by the relevant expert in the team, it is likely

that the text will be better and more accurate.

Participants of the same survey, however, had

also pointed out the disadvantages of CW including

difficult group management and coordination (Noël

and Robert, 2004), and documents that are poorly

structured. Extra coordination is needed in CW,

especially when the authors are geographically

dispersed. The team leader may need to edit the

sections contributed by the authors so that they fit

into the document. All this could lead to an increase

in the time spent, in comparison to the time required

for a single author to write the same document. The

overall coherence of the final document may also

suffer. There are several reasons why this would be

the case. For instance, some co-authors lower down

in the hierarchy may not be aware of the whole

purpose and structure of the document (e.g. a PhD

student delegated some writing by his supervisor).

The authors may just have different opinions about

how the document should be structured. The

sections thus created may not fit together properly

leading to documents that have, in the past, been

described as ‘arbitrary’ (Lowry et al., 2004). This is

the focus of our research.

Coherence is a subjective phenomenon that is

difficult to formally define. By the word

‘coherence’, we refer to the feature of a text that

makes it easy to read and understand (in addition to

the use of right grammar and language). This is

largely dependent on the sequence of the sentences

(or sections) because readers tend to draw

conclusions about the logical relationships between

adjacent sentences. When these relationships are not

obvious, the text becomes difficult to decipher.

Determining the most natural sequence in which to

present the information is not trivial. If a set of

perfectly-formed sentences are placed in various

351

De Silva N. (2007).

USING NARRATIVES IN COLLABORATIVE WRITING - An Example.

In Proceedings of the Ninth International Conference on Enterprise Information Systems, pages 351-358

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combinations, only a few (if not, just one) of them

will make sense. While this is easy to detect and

repair in a short text like a paragraph, it is much

harder in large documents, particularly when

multiple authors are involved.

While software tools assist many aspects of

collaboration excellently, support for this attribute of

writing is almost non-existent. The best effort is

made by tools that merge concurrent changes

accurately to ensure syntactic consistency (e.g.

CVS). However, syntactic consistency alone does

not guarantee coherence (De-Silva and Skaf-Molli,

2006).

There is evidence to suggest that a period of

planning can significantly enhance the quality and

coherence of a document (Torrance and Bouayad-

Agha, 2001). Such a plan would make the authors

aware of the goals and structure of the document. A

popular technique used for this purpose is outlining.

An outline is “an orderly plan…showing the division

of ideas and their arrangement in relation to one

another” (Roth, 1999). When done at the start, it

gives the authors an overall view of the structure of

the document and they can write their text

accordingly.

Figure 1: An outline for this paper.

Relationships between the ideas in an outline are

shown by indentation (main topic and sub-topic) and

the use of the same type of symbol for concepts of

equal importance (e.g. A, B, C). However, an outline

lacks explicit information as to why a certain section

was placed where it is and its relationships with the

rest of the document. In our opinion, such

information can assist authors to improve coherence.

Narrative-based writing (De-Silva and Henderson,

2005, Henderson and De-Silva, 2006) was

developed to fill this gap.

The next section briefly describes narrative-

based writing and the corresponding tool. In Section

3, we present an example that shows a team of

writers making use of narratives to plan a research

paper. Finally, the conclusions and discussions are

presented.

2 NARRATIVE-BASED WRITING

Narrative-based writing makes use of some of the

concepts from narratives in technical documents. It

is based on the idea that coherence can be linked to

the inherent ‘story’ conveyed by a document to its

readers. The more consistent the story is, the better

the document. Thereby, the method requires the

authors to begin the writing process by ruminating

on the story that their document will contain. An

explicit précis of this story is called a Document

Narrative or a DN, for short. Parts of this DN will

correspond to sections in the document.

The DN can then be analysed using Rhetorical

Structure Theory (RST) (Mann and Thompson,

1988). RST is a discourse theory developed by

linguists to analyse and improve the coherence of

texts. It asserts a hierarchical structure on the text,

based on relationships between the segments in the

text such as Motivation and Justify. It is not possible

to explain RST in detail in this paper but a short

description of it is given in section 2.2.

Finally, once the authors are satisfied with the

DN, they can get to work on the document. These

three steps are summarised below.

1. Ruminate on the story and create the DN.

2. Analyse the DN using RST in order to evaluate

and improve its coherence. The authors may

decide to repeat steps 1 and 2 until they are

convinced that the DN is the optimal for their

document.

3. Use the DN as a guide to writing the actual

document, checking that the expanded text maps

on to the DN so as to implement the story and its

RST analysis.

The rest of this section explains these three steps.

2.1 Creating the DN

A narrative, by definition, is a representation of

events (Onega and Landa, 1996, Abbott, 2002) and

has been used to refer to a wide variety of texts and

dialogues. Even though there is some debate about

the difference between a narrative and a story, they

are taken to mean the same thing in our research.

A DN is a short text, no more than half a page

long, that captures the story of a document.

Articulating a coherent DN is not always

straightforward and may require some time and

thought. However, the process of thinking about this

story first will, in our opinion, help converge

different opinions from the authors and create a

deeper understanding before writing. For larger

1.0 Introduction

2.0 Narrative-based writing

A) Creating the DN

B) Analysing the DN with RST

C) Producing the document

D) Tool

3.0 Example

4.0 Discussions and conclusions

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documents like books and theses, a DN can be

created for the whole document and then for the

subsequent chapters or sections too.

DNs for some types of documents such as a

research proposal can be found in (De-Silva and

Henderson, 2005, Henderson and De-Silva, 2006).

2.2 Analysing the DN with RST

It is now possible to analyse the DN using RST.

There are several formal theories to analyse texts

(Lehnert, 1981, Hobbs, 1985). RST was chosen for

this research because it is relatively simple, makes

use of tree structures (easier to visualise) and

provides a means of evaluating coherence. Using

RST in this way, to analyse the DN ahead of

embarking on writing the actual document, amounts

to a pragmatic method of using RST in document

synthesis, as opposed to the document analysis for

which it is popularly used.

The first step in the RST analysis is to break the

text into segments. Segments can be of arbitrary

size, but are typically clauses. Some segments are

crucial to the understanding of the text and they are

called nuclei (N). Others provide information to

support the nuclei and are called satellites (S).

Once the segments have been identified,

relationships are defined between them.

Relationships can be illustrated using diagrams as

shown below. Most relationships link a satellite to a

nucleus (so-called hypotactic relationships, e.g.

Motivation). A few relationships link multiple nuclei

(so-called paratactic relationships, e.g. Sequence).

Text coherence in RST arises due to a set of

constraints and an overall effect that is defined for

each relationship (Mann and Thompson, 1988) (see

Figure 3).

Figure 2: An example of a hypotactic relationship

(Motivation) and a paratactic relationship (Sequence).

Note that the arrows in a hypotactic relationship point

towards its nucleus.

There are 23 relationships defined in the original

paper (Mann and Thompson, 1988). However, only

nine of them have been consistently used in our

analysis of technical documents so far. They are

Background, Contrast, Elaboration, Enablement,

Evidence, Justify, Motivation and Sequence. It is

still too early to predict if this subset of relationships

is sufficient for analyses of all technical documents.

More research is required.

These relationships can be applied recursively to

form a tree called a rhetorical structure tree (RS-

tree). For instance, the DN in Figure 5 ahead has two

segments. In the RST analysis, these two segments

are linked by a SOLUTIONHOOD relationship

(segment 1 is the problem – satellite - and segment 2

is the solution - nucleus). Had the DN been larger

with more segments, the span created by this

relationship (i.e. the joining of segments 1 and 2)

could have, in turn, become involved in another RST

relationship. This continues until all the segments

are part of the RS-tree. Depending on the analyst,

there can be several valid RS-trees for a given DN.

Benefits of doing the RST analysis are:

• It is conjectured that if a text can be placed in a

well-formed RS-tree, the text is likely to be

more coherent (Marcu, 2000, Mann and

Thompson, 1988). This feature is used in this

research as a guideline for coherence in the

DNs. If the author struggles to fit the segments

into a RS-tree, it is recommended that he or she

re-think the DN.

• An RST analysis forces the authors to question

the existence and positioning of each section in

the DN. This helps to identify sections that play

no role in strengthening the document or

segments that are currently missing. This leads

to a better document.

• The tree diagrams also provide a useful

graphical representation of the narrative

structure of the document.

Motivation

Constraints on N Presents an unrealised action

Constraints on N+S Comprehending S increases the

reader’s desire to perform the action

in N

The effect The reader’s desire to perform the

action in N is increased

Figure 3: The definition for the Motivation relationship

(Source: Mann and Thompson, 1988).

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2.3 Producing the Document

After a successful analysis, the authors can be

reasonably confident that the DN is coherent and

begin constructing the document. As a general rule,

the sequence of sections in the document should

correspond to the sequence of segments in the DN.

The content of each of the sections will depend on

the RST relationships that the associated segment in

the DN is involved with. For instance, in a document

introducing a new software tool, descriptions of

successful applications of the software or positive

comments from the users may help satisfy a

Motivation relationship in the RST analysis.

2.4 Prototype of the Tool

We recognised that one way to introduce narrative-

based writing to teams of geographically-dispersed

authors would be to make it available via an easily

accessible tool. Therefore, a prototype of a tool was

built to enable co-authors to create and analyse a DN

at the start of their writing process. The rest of this

section outlines the features of this tool.

The tool was implemented as a Java Web Service

and a JSP client because this was a simple way to

create an easily accessible, shared document. The

web-based interface makes it easy for authors to

access the tool and requires no software installation

prior to use (assuming most users today will have a

Web Browser). The DN and RST relationship data

are stored in a relational database. Currently we use

Microsoft Access but are investigating alternative

databases.

Java methods were written for the following

major functions: read, edit, review and merge DNs.

It is important to note that the authors can use these

functions asynchronously without interference (more

experiments are being conducted regarding this). An

initial Business Process model for this tool can be

found in (Henderson and De-Silva, 2006). This

model has since changed to accommodate more

functionality.

• The tool contains a list of predefined DNs for

some popular types of documents (e.g. research

proposal, an abstract, a conference presentation

and so on). At the start, authors can either select

and modify a DN from this list, or create a new

DN.

• Once a DN has been created, the users are

presented with a list of tasks (e.g. edit the DN,

edit the RST analysis, review or merge DNs).

Users can select the version they wish to work on

from a drop-down list of all the versions for this

DN.

• In the tool, we have introduced the concept of the

status of a RST relationship. A relationship is said

to be “satisfied” if its nucleus and satellite fulfil

the definitions that Mann and Thompson

described for them. This is determined by the

authors. Until explicitly stated otherwise, a

relationship will remain “unsatisfied”. Similarly,

changes to parts of a RS-tree can change the status

of affected relationships to “unsatisfied”. When

the DN is displayed, all satisfied RST

relationships are shown in blue and unsatisfied

relationships in red, thus drawing the authors’

attention to sections in the DN that may need

changing. This is particularly useful since changes

made by other authors may render some parts of

the RST analysis untrue.

• Once the analysis is complete, the DN can be

reviewed. Authors can verify that all the RST

relationships are satisfied.

• When a change is made to the RS-tree, a new

version of it is created. However, only the

affected parts of the tree are copied. The

remaining parts of the tree are linked to from the

parent version. This is similar to the technique of

storing deltas (changes) in tools such as CVS

(Cederqvist, 2002) and RCS (Tichy, 1982).

• Two versions of the DN can also be merged,

particularly those derived from the same parent

version. At the moment, the merge algorithm is

very simple. In the future, a better algorithm will

be devised.

• The HTML user interface of the tool is simple

(see screenshot in Figure 4). The left frame

contains the menus, a link to the Help document

and a table showing the history of the versions of

this DN. The DN is displayed in the upper frame

on the right. A second version of the DN can be

viewed at the same time in a separate window.

This is useful for comparisons.

The DN thus produced can be used by the

authors to create the eventual document. Several

areas of the tool, especially the user interface, are

still under construction. We anticipate that this

narrative-based functionality can, in the future, be

added onto existing CW tools that already have

advanced version management and merging

algorithms (and other properties necessary for

collaboration).

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3 EXAMPLE

This section presents an example that shows how the

narrative-based technique and tool can be used by a

team of authors to structure their document. The

example is a rational reconstruction of the process

by which the paper by De Silva and Skaf-Molli

(2006) was written. The authors were in two

different countries and did not meet face-to-face to

plan it. A lot of the structure was determined by

exchanging DNs and drafts by e-mail. A fictional

third author has, however, been introduced here to

make the writing task more complex. Apart from

that, the example has been kept deliberately small so

that the necessary aspects of collaboration can be

demonstrated within the space limitations in this

paper.

The CW task involves three authors (A, B and C)

writing a joint research paper about merging

algorithms and narrative-based writing. Authors A

and B are authorities on merging algorithms while

Author C is researching on narrative-based writing.

They hope to divide the sections of the document

according to their expertise.

To start the process, Author A comes up with a

basic DN for the paper. He inputs the DN into the

tool and does a simple RST analysis of it. Both the

DN and RS-tree now become available to the other

authors (version 1). These are shown in Figure 5.

Version 1 (created by Author A)

1: Merging techniques guarantee syntactic

convergence but not the coherence of the

document.

2: Integrating merging algorithms with

narrative-based writing can fill this gap.

Figure 4: Screen shot of the tool showing version 1 of a DN for an abstract of a paper.

I. Introduction

II. The problem

III. Our solution

IV. Conclusion

Possible outline for the

paper that corresponds to

the DN above

Figure 5: Version 1 of the DN and RS-tree (created by

Author A), and a possible outline for the paper.

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The sections that need to be in the document

according to the DN are listed in the outline. Note

that the ‘Introduction’ and ‘Conclusion’ are

mandatory for most papers and are not governed by

the DN in this case (hence, they are in grey).

Sections II and III in the outline correspond to the

two segments in the DN and implement the

SOLUTIONHOOD relationship between them.

In theory, a paper with this structure should be

sufficient. However, the structure is flat and lacking

in detail. The general norm is to introduce some

background material before talking about the

research problem. However, what should this

background material be and where should it be

placed (seeing as several areas of research need to be

introduced)? In our opinion, this is where a DN can

play a major role. Trying to say the story, naturally,

will help answer some of these questions.

So, Author B responds by e-mail:

“It’s likely that many people at this

conference will be from a collaborative

writing background. While being aware

of merging techniques, narrative-based

writing will be new to them. We should

definitely include some background

material on merging techniques,

collaborative writing and, in particular,

narrative-based writing. What do you

think?”

Author B goes on to make multiple changes to

the RS-tree. She adds two new segments to the DN

and changes the RST analysis. In the tool, this

would have to be done in several stages because the

tool tracks and records every change by creating a

new version. These intermediate stages have been

omitted for simplicity and the version by Author B

has been labelled as version 2 in Figure 6. (Note

that the segments in the DNs have all been uniquely

labelled for clarity, even though some segments

appear in all the DNs.)

Note that Author B has linked two pieces of

background information into the DN. Segment 3

about collaborative writing is the background to the

problem and segment 5 about narrative-based

writing is the background to the solution. This, in

her opinion, is the most natural way to narrate this

story. These changes are accepted by the other

authors.

The SOLUTIONHOOD relationship is marked

by the tool as being unsatisfied due to the changes

made to the DN. Despite not doing a formal review

of the relationships to change its state to “satisfied”,

the authors agree that it is still valid and get started

with the writing. Authors A and B agree to do

sections I, II, III and VI. Author C gets assigned

sections IV and V. They are aware of how these

sections should be linked (dictated by the RST

relationships).

Version 2 (derived from version 1 by Author B)

Figure 6: Version 2 of the DN and RS-tree (created by

Author B), and possible outline for the paper.

Meanwhile, Author C recognises the lack of a

MOTIVATION or JUSTIFY relationship in the DN

to address the ‘So what? How is this useful?’

question that may arise in the reader’s mind. To

rectify this, Author C adds a new node and a

MOTIVATION relationship to version 1 of the DN.

She believes this will improve the document.

3: Coherence is harder to achieve in

collaborative writing when authors work on

replicas of a document.

4: Merging techniques guarantee syntactic

convergence but not the coherence of the

document.

5: Narrative-based writing is a technique to plan

coherent documents.

6: Integrating merging algorithms with

narrative-based writing can fill this gap.

I. Introduction

II. Background

III. The problem

IV. Narrative-based writing

V. Our solution

VI. Conclusion

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Version 3 (derived from version 1 by Author C)

Figure 7: Version 3 of the DN and RS-tree (created by

Author C).

In Figure 7, note that segments 8 and 9 are

involved in a MOTIVATION relationship. Segment

9 provides the motivation to carry out the research

outlined in segment 8. Section IV in the outline

corresponds to segment 9 in the DN.

The other authors realise the usefulness of a

MOTIVATION relationship in the DN and agree that

it is an essential component of a winning paper.

However, they still think the background material in

Version 2 is important too. Seeing that both version 2

and version 3 were derived from version 1, they use

the tool to merge the two DNs to produce the results

below (version 4 in Figure 8).

The authors are confident with this merged

version. The RST relationships are all still valid

(even though the tool has marked SOLUTIONHOOD

as “unsatisfied” according to the implemented

protocol). The scene for the paper is set by sections II

and III which will explain why coherence is harder in

CW, give an overview of the shortcomings of current

merging techniques and describe how this affects

documents. Next, the solution is introduced together

with a short tutorial on narrative-based writing which

is necessary to fully comprehend the nature of the

proposed work.

Version 4 (merged from versions 2 and 3

by Author A)

Figure 8: Version 4 of the DN and RS-tree (created by

Author A).

The Benefits section can contain applications or

examples of where the solution will help the

existing situation. This will be the motivation that

led to the development of these ideas.

For the actual paper, the DN was changed again

so that the satellite of the MOTIVATION

relationship preceded the solution and so on.

However, we stop the example here because the

essential attributes of how the DN and the tool can

assist in planning a document have been shown.

7: Merging techniques guarantee syntactic

convergence but not the coherence of the

document.

8: Integrating merging algorithms with narrative-

based writing can fill this gap.

9: This is a unique solution that helps writers

produce better documents.

I. Introduction

II. The problem

III. Our solution

IV. Benefits

IV. Conclusion

10: Coherence is harder to achieve in

collaborative writing when authors work on

replicas of a document.

11: Merging techniques guarantee syntactic

convergence but not the coherence of the

document.

12: Narrative-based writing is a technique to plan

coherent documents.

13: Integrating merging algorithms with

narrative-based writing can fill this gap.

14: This is a unique solution that helps writers

produce better documents.

I. Introduction

II. Background

III. The problem

IV. Narrative-based writing

V. Our solution

VI. Benefits

VII. Conclusion

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4 DISCUSSIONS AND

CONCLUSIONS

Coherence is the attribute of a document that makes

it easy to understand, and is often harder to achieve

in CW because multiple authors are responsible for

the content. Coherence can be linked to the implicit

story conveyed by the document to the readers.

However, software support for this aspect of writing

is almost non-existent. Even planning techniques

such as outlining do not seem to adequately address

the problem. Narrative-based writing is a planning

technique that was introduced to address this

problem. It involves creating a DN and analysing it

using RST to evaluate and improve its coherence.

The segments in the DN correspond to sections in

the document. We claim that a better DN will lead

to a better document.

The DN provides a way of quickly discovering

the natural progression of concepts in a document.

The authors need to think of the best possible story

that their ideas can be fitted into. The corresponding

RST analysis gives some evaluation of the story’s

coherence and also helps point out ill-fitting story

segments and better alternatives. When several

authors have opinions on the content of the paper,

forcing themselves to create a DN helps combine

these ideas into a coherent whole.

A tool has been built to help authors engage in

narrative-based writing. The tool helps manage the

versions, store the RS-trees and draw the authors’

attention to unsatisfied relationships (particularly

beneficial in large analyses). This paper has

presented an example that shows how this tool and

technique can be used by a team of authors. The

changes in the DN made by each author are reflected

in the plan for their document. The DN is updated

until all the co-authors are confident that it is the

most effective for the purpose of their document.

Existing CW tools like CVS already have

advanced features to manage and merge versions,

making it unnecessary for us to re-visit these areas in

our implementation. What existing tools appear to

lack is support for coherence. We anticipate that the

inclusion of DNs and support for RS-trees in these

tools will bridge this gap, and assist co-authors even

further.

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