The Influence of Various Text Characteristics on the Readability and

Content Informativeness

Nina Khairova

, Anastasiia Kolesnyk

, Orken Mamyrbayev

and Kuralay Mukhsina

National Technical University ”Kharkiv Polytechnic Institute”, 2, Kyrpychova str., 61002, Kharkiv, Ukraine

Institute of Information and Computational Technologies, 125, Pushkin str., 050010, Almaty, Republic of Kazakhstan

Al-Farabi Kazakh National University, 71 al-Farabi Ave., Almaty, Republic of Kazakhstan

Keywords: Text Quality, Readability Indexes, Linguistic Features, Statistical Characteristics of a Document, Simple

Wikipedia, Enterprise Information Systems.

Abstract: Currently, businesses increasingly use various external big data sources for extracting and integrating

information into their own enterprise information systems to make correct economic decisions, to understand

customer needs, and to predict risks. The necessary condition for obtaining useful knowledge from big data

is analysing high-quality data and using quality textual data. In the study, we focus on the influence of

readability and some particular features of the texts written for a global audience on the texts quality

assessment. In order to estimate the influence of different linguistic and statistical factors on the text

readability, we reviewed five different text corpora. Two of them contain texts from Wikipedia, the third one

contains texts from Simple Wikipedia and two last corpora include scientific and educational texts. We show

linguistic and statistical features of a text that have the greatest influence on the text quality for business

corporations. Finally, we propose some directions on the way to automatic predicting the readability of texts

in the Web.

1 INTRODUCTION

Nowadays, industry, government and businesses

increasingly use various external big data sources in

order to extract and integrate information into their

own enterprise information systems (Cai, 2015).

By analyzing a massive amount of information

and knowledge from various external sources, a

decision-maker has benefits for making correct

financially significant economic decisions,

understanding customer needs, predicting and

preventing any risks.

However, the necessary condition for obtaining

useful knowledge from big data is analysing data

high-quality, in particular, using qualitative textual

data. At the same time, the Internet is flooded with

different texts that convey no useful information for

business purposes. It can be not only meaningless

blogs and obvious computer-generated spam but also

https://orcid.org/0000-0002-9826-0286

https://orcid.org/0000-0001-5817-0844

https://orcid.org/0000-0001-8318-3794

https://orcid.org/0000-0002-8627-1949

such texts that, at first glance, are from reliable and

serious sources.

Although today the concept of the quality of the

text information is quickly modified depending on the

type of information, its style, and field of

applications, and besides, the universal method for

conducting a full assessment of the quality of textual

material has not been developed yet, mainly, the

estimating of the text information is based on

traditional quality assessment standards of

information generally relevant to actual business

needs. It is believed that such text information quality

dimensions are аvailability, usability, reliability,

relevance and presentation quality.

In our study, we focus on the last dimension,

which we can divide into such elements associated

with it as readability, structural and linguistic

correctness. We strive to reveal parameters to identify

462

Khairova, N., Kolesnyk, A., Mamyrbayev, O. and Mukhsina, K.

The Inﬂuence of Various Text Characteristics on the Readability and Content Informativeness.

DOI: 10.5220/0007755004620469

In Proceedings of the 21st International Conference on Enterprise Information Systems (ICEIS 2019), pages 462-469

ISBN: 978-989-758-372-8

these elements of the text quality and develop a tool

for testing it.

At the same time, in many cases, text sources,

which are used by enterprise information systems,

address a global audience. That is, they are written in

English and then receive worldwide distribution. For

these Internet resources, in order to estimate its

readability, we offer to add such linguistic features to

traditional readability level indexes as the use of one-

word verbs instead of a verb phrase or the use of only

international writing of terms.

As an example of the texts intended for the

worldwide audience, we employ our corpus of

Wikipedia articles. Currently, there are sufficient

approaches to quality assessment of Wikipedia

articles (Lewoniewski, 2017). The issue of Wikipedia

texts quality assessment has become the subject of

studies in various fields of science. In 2006 one of the

co-founders of the online non-profit encyclopedia

Wikipedia suggested concentrating on the quality of

the articles instead of their number (Giles, 2005).

The best articles of Wikipedia must follow the

specific style guidelines, the rating system of which

depends on a specific language. For example, in

English Wikipedia articles, which we examine in the

study, the system of Wikipedia articles quality has 9

grades: FA (Featured Article), A, GA (Good Article),

B, C, Start, Stub, FL (Featured List), List. Each of

these grades has special criteria. For instance, to those

criteria, we can include the relevance,

informativeness and encyclopedicness (Khairova,

2018) of the information, the correctness of texts

spelling and grammar and some others. However, to

date, all of these criteria are assessed manually by the

Wikipedia communities.

In our study, we will consider the influence of

readability and some particular features of the texts

written for a global audience on the texts quality

assessment.

In order to estimate the influence of different

linguistic and statistical features on the text

readability, we decided to use five different text

corpora.

2 RELATED WORK

The readability concept was introduced in the 1920s

and it means the ability to read a text. Until the late

1980s, the readability concept was used by educators

in order to identify the complexity of tutorials and

textbooks. The educators discovered a way to use

vocabulary difficulty and sentence length to predict

the difficulty level of a text (DuBay, 2004).

At the present time, readability is one of the

dimensions of the text information quality and it

matters in every profession where people need

qualitative information and knowledge. Now, the

most known ways of representation of readability

level are 5 indexes, such as Flesch Reading Ease

(Cotugna, 2005), Flesch-Kincaid Grade Level, ARI

(Oosten, 2010), SMOG (Hedman, 2008) and FOG

(Walsh, 2008).

Generally, more modern methods are based on the

data of well-known indexes and do not give a reliable

advantage to any of them.

For instance, Pitler and Nenkova (Pitler, 2008)

ranged the influence of various readability factors on

predicting readability of a text and the text quality

Schwarm and Ostendorf (Schwarm, 2005)

proposed to develop new method appropriate for

finding English texts of a certain readability level on

the basis of the widely known readability indexes to

combine them with statistical language models,

support vector machines and other language

processing tools. Their research showed that

сombining information from statistical LMs with

other features using support vector machines

provided the best results.

Authors of the next study (Oosten, 2010) used 4

corpora in two languages, Dutch and English to find

the correspondences between the readability formulas

and variables that are used in them.They made a

conclusion that it was not reasonable to expect that

formulas based on language-independent features can

precisely predict the readability level.

It is interesting, that many studies dedicated to

readability analyze the text readability on the basis of

the texts devoted to health care. In our opinion, that's

because such texts must be understandable to as many

readers as possible. In medicine, it is extremely

important that texts with such information correspond

to the average level of the reader. The United States

Department of Health and Human Services identified

that the reader of this level is in the 7th-grade

(D'alessandro, 2001).

According to the article (D'alessandro, 2001), the

average reading level is eighth-ninth grade, in the

USA. But all medical education materials are too

complex for average adults. It means that such

materials should have a lower grade to be

understandable. Their conclusion was based on the

result of 2 most widely used indexes: The Flesch

Reading Ease score and Flesch-Kincaid that

evaluated one hundred documents from 100 different

Web sites. The result was that pediatric patient

education materials on the Internet were not written

at an appropriate reading level for the average adult.

The Inﬂuence of Various Text Characteristics on the Readability and Content Informativeness

463

One more article that also affected health topic

(Walsh, 2008) confirmed that the average readability

of Internet-based consumer health information had

exceeded the recommended 7th-grade reading level.

They assessed articles with 3 readability indexes:

FOG, SMOG and Flesch-Kincaid.

The second element, which associated with the

presentation quality of textual information, is

structural and linguistic correctness of a text. The

most universal criteria for grammatical, punctuation,

and style evaluation of technical and scientific texts

are offered by The Microsoft Manual of Style

(Microsoft, 2012).

The glossary of the international version of the

terminology spelling developed by the INTECOM

International Language Project Group is very useful

in this regard (Intecom, 2003).

The objective of INTECOM’s International

Language Project Group was to identify which

spelling and usage it should recommend for

documentation that would be written in English and

would receive worldwide distribution.

Additionally, nowadays a lot of web-resources

provide online services to assess the readability of a

text. Site ReadablePro

, website Online-Utility.org

textalyzer

tools are the best-known type of such

resources. However, a detailed examination of such

resources revealed that the results of their work are

different when one and the same text is checked on

readability. So it can be said that the resources the

Internet offers us today can provide non-

representative and unreliable results.

3 EXPERIMENTS AND RESULTS

3.1 Experiments Description

Generally, the readability of the texts, submitted on

the Internet, is affected by many factors. For example,

it can be rhythmics of the text, the complexity of the

used words and sentences, website logical structure (a

background, types of fonts, the sizes of columns,

etc.). Neglecting any of these parameters can

significantly reduce the readability of the Web article.

Besides, the readability level (or the complexity

of text perception) is influenced by such linguistic

features of the text as length of words and sentences;

the complexity of syntactic constructions; the rate of

Measure readability: https://readable.io/

Test document readability: https://www.online-utility.

org/english/readability_test_and_improve.jsp.

Improve text readability: https://www.textalyzer.xyz

words; the level of abstractness of lexicon; the large

number of terms; the use of neologisms and jargons;

active or passive voice.

These indicators are used for formulation of

various formulas of readability indexes calculation. In

our experiments, we calculate a few main coefficients

of readability that can be used to compute readability

of any texts types: scientific, education, encyclopedic

and some others.

Fog Index is calculated by the following equation:

FOG= 0.4





+100





(1)

where w is the number of words, s is the number of

sentences and cw is the number of complex words.

The SMOG index doesn’t need the entire text to

be assessed. It requests 10 sentences in a row near the

beginning, 10 in the middle, and 10 in the end:

SMOG=1.0430



∗





+3.1291,

(2)

where NPS is the number of polysyllables and

s is the

number of sentences.

ARI index outputs a number that approximates

the age needed to understand the text.

ARI = 4.71





+ 0.5





– 21.43,

(3)

where c is characters (the number of letters and

numbers), w is the number of words and s is the

number of sentences

To determine the readability level of the scientific,

educational, encyclopedic Web resources addressed

to a world audience, we add the linguistic features to

the traditional coefficients described above. Then we

divide these features into three group potential

mistakes. In our research, we use three types of these

mistakes that can influence the text quality. These are

punctuation mistakes, grammar mistakes and style

mistakes.

Table 1 shows the distributions of some linguistic

features according to these groups of mistakes.

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464

Table 1: The distributions of some linguistic features according to the types of mistakes.

Style mistakes Grammar mistakes Punctuation mistakes

Writing of digits from 1 through 9 in

words

Use of one-word verbs instead of a

verb phrase

Use of only one gap after the

punctuation mark.

Use of numerals for 10 and greater Use of only international writing of

terms

There is no coma in MMMM YYYY

date format

Use of numerals for all measurements,

even if the number is less than 10

Use a punctuation mark without an

extra gap

Use of (from і through) instead of

(between і and)

Slash cannot be a substitute of “or”,

for example he/she

Use of MMMM DD, YYYY date

format

Use of italic formatting instead of

upper-case

No abbreviation of months

3.2 Source Data

The dataset of our research includes 5 corpora of three

styles, namely, educational, scientific and

encyclopedic. Table 2 shows the distributions of the

analyzed articles

according to our corpora.

Table 2: The distribution of analyzed articles according to

our corpora.

Corpus

name

category

The

number

of words

Good-

Enough

358454

Needs-

Work

Stub

189885

Simple-

Wikipedia

15 8611

Education

Astronomy

Biology

Chemistry

Physics

31755

Science Astronomy

Biology

Chemistry

Physics

33024

Three of our corpora consist of articles from

English Wikipedia. This Web-resource was chosen

for our experiment because nowadays Wikipedia is

the biggest public universal encyclopedia. And it

means that Wikipedia’s articles must be well-written

and must follow style guidelines. But Wikipedia isn’t

a static resource. Anyone can make changes and it can

well affect the article quality. All experts admit that

there are some difficulties in determining the

Wikipedia articles quality. In our research, we intend

to estimate their quality and check their readability

level. To obtain the texts from Wikipedia, we have

created own special software for automatic parsing of

the websites.

The first corpus, “GoodEnough” consists of 90

articles that belong to such quality classes of English

Wikipedia as Featured articles (FA) and Good articles

(GA). All of these articles must have correct grammar

and spelling.

For the second corpus, “NeedsWork”, we chose

also 90 articles from such quality classes as C and

Stub, that are very underworked and need further

completion.

The third corpus includes 15 articles from the

Simple English Wikipedia. The Simple Wikipedia is

a resource that is much easier to understand for

children and adults who are learning English (Coster,

2011). It is free and all articles are based on basic

English vocabulary and grammar and shorter

sentences.

In order to compare the readability of Wikipedia

articles and texts from other information sources, we

have produced two further corpora, which also

comprise educational and scientific texts.

The first one is called “Education” and includes

20 different texts from school books (from 6th

through 12th grades) and college books (all years of

education) on such topics: Physics, Astronomy,

Biology, Chemistry.

The second supplementary corpus, which is

called “Science”, is created on the basis of scholarly

articles from GoogleScholar and other scientific

internet resources. It includes 20 different texts on

Physics, Astronomy, Biology and Chemistry

subjects.

The Inﬂuence of Various Text Characteristics on the Readability and Content Informativeness

465

3.3 Experimental Evaluation

In order to estimate the readability of our corpora

texts, we determine three groups of features. The first

one comprises three traditional indexes of readability.

These are FOG (1), SMOG (2) and ARI (3).

The second group of the features that in our

opinion can impact on a level of readability of a text,

which is addressed to a global audience, includes

linguistic mistakes in the text. The three types of

them, which we have identified above, are shown in

Table 1.

The third group of the features that affect

readability text level comprises conventional

statistical characteristics, such as the number of

nouns, the number of pronouns, the number of unique

words, the number of sentences that includes more

than 30 syllables etc. To calculate the third group of

features, we carried out the POS-tagging of our

corpora using the nltk

package of Python.

Table 3 shows values of the features of all these

three groups for our five corpora. Additionally, based

on the Corpus Linguistics approaches (McEnery,

2012), (Rizun, 2018) in order to compare the

frequencies of linguistic features occurrence in the

different corpora, we normalized their frequencies per

10 thousand words. All numbers in tables, graphs and

figures represent the normalized frequencies of the

emergence of these features in the text corpus.

Every readability index has its own assessment

scale with different values. For example, SMOG

depends on the number of words with three or more

syllables and this number is compared with a grade

level. FOG index is based on a grade level and ARI

depends on age. In order to compare the results of our

research on 5 corpora, we created a universal scale

based on the grade levels and factual values of our

indexes. Table 4 shows our universal scale of the

readability index.

Table 3: The values of the features of three groups (the traditional indexes, linguistic characteristics, statistical characteristics)

for our five corpora.

Criterion GoodEnough NeedsWork Simple

Wikipedia

Science Education

Punctuation mistakes 3.16 3.09 4.72 4,71 0,84

Grammar mistakes 2.24 2.12 2.98 4,42 7,37

Style mistakes 6.84 7.28 9.44 4,48 1,95

Sentences > 30 syllables 50.7 50.4 31.5 58,6 45,1

Words (4 syllables) 76.9 72.4 81.0 73,8 83,8

Words (12 letters) 76.9 72.4 81.0 73,8 83,8

Passive voice 31.6 30.4 34.8 35,3 41,4

The number of unique words 124.0 190.0 153.1 340,6 326,3

The number of adjectives 169.5 170.5 196.4 252,3 222,1

The number adverbs 64.2 64.9 86.7 80 89,5

The number of nouns 830.1 836.6 806.9 638,5 683,2

The number of conjunctions 45.9 51.4 47.2 44,6 56,2

The number of verbs 353.6 335.2 360.3 302,4 347,8

The number of prepositions 252.2 247.2 256.1 250,8 253,4

The number of pronouns 20.3 20.9 30.8 23,6 37,5

The number of determiners 213.2 210.2 264.1 221,8 253,4

ARI

10.9 11.8 7.6 13,9 9

SMOG

115 128 58 187 122

FOG

9.614 10.57 8.548 12,71 9,622

Natural Language Toolkit: https://www.nltk.org/

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Table 4: The universal scale of the readability index based on the grade levels and factual values of FOG, ARI and SMOG

indexes.

Grade level FOG ARI SMOG

Graduate 17 14 211-240

College student (4-5 year) 15-16 13 157-210

College student (2 year) 13-14 12 111-156

High school (11

- 12

grades) 12-10 11 43-90

Secondary school (9

-8

grades) 9-8 10-9 21-42

Secondary school (6

-7

grades) 7-6 8-7 7-20

Figure 1: The values of FOG, ARI and SMOG indexes for GoodEnough Wikipedia, NeedsWork Wikipedia, Simple

Wikipedia, Science, Educational corpora.

According to the table, we built a histogram that

combines all researched indexes in conformity with

their factual values and allows demonstrating clearly

differences between our corpora.

Figure 1 shows the values of FOG, ARI and

SMOG indexes for our five corpora, namely for

GoodEnough Wikipedia, NeedsWork Wikipedia,

Simple Wikipedia, Science, Educational.

Our experiments show that the dependence of

FOG and ARI on types of the texts corresponds to

intuitively expected. According to the values of these

indexes, texts that have the smallest level of

complexity are the texts from SimpleWikipedia and

Education corpora. By the complexity level, these

texts correspond to the high and middle school level.

Scientific texts and texts from NeedsWorkWikipedia

corpus are the most difficult for reading. These texts

are intended for college students.

According to figure 1 SMOG index represents

less obvious results. The main reason of this is that

SMOG index is usually calculated on a limited

fragment of the text (30 sentences). Despite this

restriction, according to the values of this index texts

with the lowest level of readability are the texts of

SimpleWikipedia, and texts with the highest

readability level belong to the corpus with scientific

articles.

Accordingly, based on these indexes, we can infer

that, by the reading complexity level, our five corpora

can be arranged from the simplest to the most difficult

as follows:

• Simple Wikipedia,

• Education,

• GoodEnough Wikipedia,

• NeedsWork Wikipedia,

• Science

Based on the results of the table 3, we can

conclude that among statistical characteristics of the

The Inﬂuence of Various Text Characteristics on the Readability and Content Informativeness

467

text the frequency of emergence of the sentences

which have more than 30 syllables has the greatest

influence on the readability level. We can see that

SimpleWikipedia corpus has the smallest results

(31.5), and Education corpus has the highest results

(58.6).

At the same time, the number of long words (more

than 4 syllables or more than 12 letters) does not have

such great influence on the reading complexity as it

was considered earlier. Also, such feature as the

number of unique words in the text is interesting too.

There is the lower number of such words in

SimpleWikipedia than in texts from Science and even

Education corpora.

The large number of unique words in texts of

NeedsWork corpus is explained by the fact that these

texts don’t have a large size.

Reviewing the influence of the second group of

the analyzed factors, namely linguistic mistakes, on

the text readability, it is possible to see that

grammatical mistakes are the most linked to the text

complexity.

The number of such mistakes is much more in

Education corpus and less in the Wikipedia articles.

It is interesting that Wikipedia texts took a

midpoint position on the number of punctuation

mistakes.

The greatest percentage of style mistakes was

revealed in Wikipedia articles the number of which

does not depend on the user assessment of the article

quality. Generally, the discrepancy to the accepted

international way of writing of dates, numbers and

units of measure worsens style of the text and

therefore its readability.

4 CONCLUSIONS

In connection with the fast development of

information resources, a lot of enterprises and

corporations actively use big data sources not only for

obtaining information but also for extracting and

integrating information into their own enterprise

information systems. For making business decisions

it is necessary to be based on pure information and in

particular, take it from qualitative texts (information

texts).

The quality of texts includes a big number of

various characteristics, for example, usability,

reliability, relevance, availability and also readability.

Assessment of the text complexity is not

connected with a lack of knowledge of the subject or

material complexity. It is connected with the logical

organization of the text, linguistic characteristics, the

complexity of grammatical structures, vocabulary

and sentence construction.

In our study, we analyzed readability indexes for

English-language texts and revealed linguistic criteria

of text information quality. Such characteristics are

divided into 3 types: grammar, punctuation and style.

Our grammar criteria are based, for instance, on the

use of one-word verbs instead of a verb phrase and

the use of only international writing of terms.

Punctuation criteria consist of such items as the use

of only one gap after the punctuation mark, the

absence of coma in MMMM YYYY date format, the

use of a punctuation mark without an extra gap and

the use of “or” in structures specifying choice. (for

example he/she). Style criteria are responsible for the

writing of digits, measurements, date format and

abbreviations.

Results of the analysis were applied to

Wikipedia articles that belong to three classes

according to the assessment scale of the co-founders

Wikipedia, and to texts of the educational and

scientific direction. We consider that articles from

Wikipedia can really be assigned both to scientific

and to educational resources that correspond to its

rank of an encyclopedic resource.

According to the results of our research, it is

possible to make a conclusion that such indexes as

FOG and ARI evaluate texts on their complexity

more accurately. SMOG index gives rather accurate

results, but only on small fragments of the text that

can complicate the process of assessment.

Also, we suppose that grammatical mistakes are

the most connected with the text complexity because

grammar is responsible for sentence structure.

Therefore, we can claim that Wikipedia articles are

easier for reading than texts from the Education

corpora which contain the largest number of

grammatical mistakes. In turn, Wikipedia articles

have the highest percentage of style mistakes because

they consist of Americanisms. And they have an

average result on punctuation mistakes.

Besides, our analysis indirectly confirms that

Wikipedia articles are an academic resource. The

experiment entirely confirms a hypothesis of the

science paper (Biber, 1999) about the fact that in

academic prose nouns are by far the most frequent

word class; on average every fourth word is a noun.

Verbs are less frequent, on average every tenth word

is a verb, followed by adjectives or adverbs. In the

same time, it is obvious that the experiments show

almost full independence of readability from the

number of particular parts of speech in the text.

The study allows not only estimating the quality

and the readability of text information but also using

ICEIS 2019 - 21st International Conference on Enterprise Information Systems

468

its result for further improvement of the text

information.

In future work, we plan to expand styles of text

corpora and the number of criteria for the text quality

assessment. These improvements will contribute to

the analysis of big data from Internet resources and

will allow creating the qualitative content of such

resources.

ACKNOWLEDGEMENTS

This research is supported by the Committee of

Science of the Ministry of Education and Science of

the Republic of Kazakhstan. The research was funded

by grant number AP05131073 — Methods, models of

retrieval and analyses of criminal contained

information in semi-structured and unstructured

textual arrays.

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