How to Simplify Law Automatically? A Study on South Korean
Legislation and Its Simplified Version
Stefanie Urchs
, Akshaya Muralidharan
and Florian Matthes
Chair of Software Engineering for Business Information System, Technical University of Munich, Garching, Germany
Text Simplification, Legal Tech, South Korean Legilsation, Natural Laguage Processing.
People with a low literacy level have problems understanding complex texts. Especially legal texts can be
challenging. Automatic Text Simplification (TS) can help to make the legal text more accessible. However,
most TS research is based on Wikipedia articles and newspaper articles. To be able to use automatic TS on
the legal text we have to understand what constitutes simple legal text. Therefore, we examine the English
translation of South Korean legislation and its official simplification. Subsequently, we use state of the art TS
models on the legislation text. The models simplify the text only quantitatively lacking in retaining the context
of the original text.
Text Simplification (TS) is a Natural Language Pro-
cessing task that aims to automatise the tedious work
attached to manual text simplification. Al-Thanyyan
and Azmi (Al-Thanyyan and Azmi, 2021) define TS
as [...] the process of reducing the linguistic com-
plexity of a text to improve its understandability and
readability, while still maintaining its original in-
formation content and meaning.”. Generally, TS
includes the following tasks: simplifying the form
and content of a text, reducing redundancies and not
needed information from a text and summarisation of
a text (Siddharthan, 2015). TS opens documents up
to people with a lower literacy level. A study by the
”Program for the International Assessment of Adult
showed that 32% of U. S. adults are
able to read and extract information from simple texts
but are overwhelmed by more difficult tasks. Being
unable to understand more complex texts is especially
critical when legal texts are involved. If a person does
not understand the law they will have problems ad-
hering to it. Furthermore, people who are not able to
understand the rights, granted to them by the law, are
not fully able to use them. Rubab et al. (Rubab et al.,
2020) show that simplifying the wordings of law in-
creases the comprehensibility of the texts for law pro-
1 accessed on 2021-10-15
fessionals, law teachers and laymen alike, making it
more accessible.
However, simplifying legal text is no easy task, as
all the meaning of the source text has to be preserved.
Therefore, TS in the legal field is in need of care-
fully constructed training data and metrics to detect
if a text is successfully simplified while retaining all
crucial information. Unfortunately, training corpora
for normal TS task are already scarce. To the authors’
knowledge currently, no corpus of normal legislation
aligned to a simplified version of this legislation ex-
ists. Hence research on automatic simplification of
legislation is scarce too. Normal TS measures do not
necessarily apply to the legal field and its sensitivity
to semantics in text. Consequently, TS models that
are optimised towards common TS measures do not
necessarily apply to the legal field.
The South Korean government provides its citi-
zens with an online version of their legislation
after referred to as E-Law) and a translation into En-
glish. Moreover, the South Korean government pro-
vides a simplification of its legislation
(hereafter re-
ferred to as Easy-Law) which is translated into twelve
different languages, among them English. We align
Easy-Law with its corresponding E-Law parts. Sub-
sequently, we analyse how an easy to read legal text,
that contains all necessary information, compares to
its standard law counterpart. Therefore, we examine
what constitutes easy legal language. Additionally,
2 accessed on 2021-10-28
3 accessed on 2021-10-28
Urchs, S., Muralidharan, A. and Matthes, F.
How to Simplify Law Automatically? A Study on South Korean Legislation and Its Simplified Version.
DOI: 10.5220/0010894000003116
In Proceedings of the 14th International Conference on Agents and Artificial Intelligence (ICAART 2022) - Volume 3, pages 697-704
ISBN: 978-989-758-547-0; ISSN: 2184-433X
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
we use state of the art TS models on E-Law and com-
pare them to the Easy-Law baseline. Due to copyright
issues, we can not publish the parallel dataset.
This paper analyses the simplification of South Ko-
rean legislation. Therefore, this chapter introduces re-
lated work from the fields of text simplification (TS),
TS corpora and metrics to analyse the simplicity of
the text.
2.1 Text Simplification
In one of the earliest works on TS Chandrasekar and
Srinivas (Chandrasekar and Srinivas, 1997) approach
the task on a sentence level. The authors propose
a two-step process: first, the input is analysed and
its structure is described in a dependency tree with
the Tree Adjoining Grammar (TAG). In the second
step this description is used to transform the input
into a simpler version, basically flattening the depen-
dency trees. The transformation rules are automati-
cally learned on an aligned corpus of sentences and
their manual simplification. Dars (Dras, 1999) ad-
dresses TS on a text basis and defines the problem as
”reluctant paraphrase”. His base assumption is that an
author produced a text that contains everything they
want to express. Due to external factors like length
constraints, readability issues or style guides authors
have to paraphrase their text. Therefore, a ”best solu-
tion” for the paraphrase, one that adheres best to the
factors and still retains the most textual meaning, ex-
ist. Like Chandrasekar and Srinivas, Dars uses the
TAG to represent the input. He then proceeds to map
between two TAG grammars, combined with integer
programming, to adhere to the previously mentioned
factors. At the same time, he tries to paraphrase as
little as possible. Both approaches concentrate on the
syntactic simplification of text. Other researchers in-
terpret TS as a monolingual translation task. For ex-
ample, Wubben et al. (Wubben et al., 2012) integrate a
re-ranking heuristic into the translation task and train
their system on a corpus that aligns Wikipedia articles
with their corresponding Simple Wikipedia articles.
A third way to approach TS is to lexically simplify
the text at hand. One of the current state of the art TS
systems by Qiang et al. (Qiang et al., 2021) is a lexi-
cal simplification model based on BERT. The system
pipeline consists of three steps: first identifying com-
plex words in the text, second generating words to re-
place these complex words and third filtering the gen-
erated words and ranking them according to the best
replacement fit. The resulting model is called LSBert.
Martin et al. (Martin et al., 2020a) approach TS as a
mixture of simplifying the structure and grammar of
a text. Their TS system ACCESS enables the user to
tune the text simplification to the following attributes:
sentence length or amount of compression, level of
paraphrasing and reduction of lexical and syntactic
complexity. A Sequence-to-Sequence model is then
conditioned on these attributes. In later work, Mar-
tin et al. (Martin et al., 2020b) extend their ACCESS
model by the MUSS method. This method trains con-
trollable TS models in an unsupervised way, by util-
ising sentence-level paraphrase data. Therefore, ex-
cluding the need for labelled training data.
At the time this paper is written LSBert and AC-
CESS in combination with MUSS can be considered
state of the art. Therefore, this paper uses these mod-
els on the E-Law corpus and compares them to the
Easy-Law baseline, investigating their compatibility
to legal language.
2.2 Text Simplification Corpora
TS literature uses parallel text corpora. These are
corpora where a complex source is aligned with its
simplification. In the field of English TS corpora
mostly use a combination of the English Wikipedia
with Simple Wikipedia. For example Coster and
Kauchak (Coster and Kauchak, 2011) aligned sen-
tences from English Wikipedia with their simplifica-
tion from Simple Wikipedia resulting in 137 thousand
sentence pairs. Zhu et al. (Zhu et al., 2010) do the
pairing of the Wikipedias on an article basis and ob-
tain 65,133 paired articles. A third paired Wikipedia
corpus is generated by Kajiwara and Komachi (Kaji-
wara and Komachi, 2016) by performing a match on
the article titles. Their corpus consists of 126,725 ar-
ticle pairs. Xu et al. (Xu et al., 2015) argue that the
combination of Wikipedias is insufficient as TS re-
source and propose the Newsela corpus. This corpus
consists of 1,130 news articles, that are professionally
rewritten in four different simplification grades. The
OneStopEnglish corpus by Vajjala and Lu
and Lu
c, 2018) aligns 189 texts over three reading
levels, resulting in 567 texts that are aligned at text
and sentence level.
Unfortunately, none of these TS corpora covers le-
gal text.
2.3 Text Simplification Metrics
To evaluate TS systems metrics have to be used.
Dars (Dras, 1999) names three formulas as commonly
known readability metrics for adult reading material:
ICAART 2022 - 14th International Conference on Agents and Artificial Intelligence
Flesch Reading Ease
reading ease = 206.835 0.846w
Subtracts the average number of words per
) from the number of syllables per 100
words (w
), the constants norm the reading ease to
a grade based number.
Dale-Chall Formula
= 0.1579x
+ 0.0496x
+ 3.6365
is the reading grade score of a student who
knows half of the answers for questions about a
text paragraph. It is calculated by adding the per-
centage of words outside a predefined easy to read
list (x
) to the average sentence length in words
FOG Index
reading level = 0.4x
+ 0.4x
Adds the average sentence length x
to the per-
centage of words with more than three syllables
Al-Thanyyan and Azmi (Al-Thanyyan and Azmi,
2021) extend this list by the following formulas:
SMOG grading score
SMOG = 3 +
Looks at the average of polysyllable words (x
(words with more than two syllables) in 30-
sentence-long text segments.
Flesch-Kincaid Grade Level Index (FKGL)
FKGL = 0.39x
+ 11.8x
Combines the average length of a sentence (x
and the average number of syllables per word (x
and uses the constant to calculate a grade level.
This paper uses these metrics to understand what
constitutes easy legal language, using the Easy-Law
corpus. Furthermore, the metrics are used to analyse
the text generated by LSBert and MUSS in combina-
tion with ACCESS.
The Korean Legislation Research Institute (KLRI)
provides a global platform for legislative research.
The KLRI cooperates with the Korean Law Transla-
tion Center (KLT)
to offer an English translation of
current Korean statutes (referred to as E-Law in the
remainder of this paper). KLRI and KLT aim to pro-
mote understanding of Korean Legislation for a global
4 accessed on 2021-10-30
5 service/ ac-
cessed on 2021-10-30
audience and non-Korean speaking residents. The
Korean Ministry of Government Legislation
this online resource with a website
that explains Ko-
ran legislation in an easy to understand way. The
website (further referred to as Easy-Law) provides in-
formation in Korean and twelve different translations,
among them English.
This chapter introduces the E-Law and Easy-Law
websites and a parallel corpus we created by aligning
acts from these two websites.
3.1 E-Law
The E-Law website provides a translation of current
legislation and historic legislation. We concentrate on
the current legislation, where 2,230 statutes and regu-
lations are provided. Users can search the website in
different ways:
By Legal Field
Twelve different fields, from the constitution to
foreign affairs, are offered.
By Ministry
Users can search for legislation for one of 23 Min-
istries, one of 17 Agencies or Administration, one
of six commissions, one of six constitutional in-
stitutions or one of five miscellaneous options.
By Subject
The website offers 16 different subjects includ-
ing family law, launching business, employmen-
t/labor, culture and environment/energy.
All Overview of all legislation with keyword
search. The sidebar enables searching by legal
field, ministry and subject as well as validity (cur-
rent and historical legislation) and type of statutes
and regulations.
Recently Translated
Listing of all legislation ordered by date of the
Most viewed
Listing of all legislation ordered by the number of
total hits either by week, month or year.
Law in News
List of all legislation that is featured in the news.
In addition to the translation of legislation, the KLT
offers a glossary of legal terms which lists certain key-
words and provides a full-text search for these in all
statutes. Furthermore, the Korean legislative system
and legislative procedures are explained.
6 accessed on 2021-10-30
langCd=700101 accessed on 2021-10-30
How to Simplify Law Automatically? A Study on South Korean Legislation and Its Simplified Version
3.2 Easy-Law
In contrast to the E-Law website, the Easy-Law web-
site is less structured. Therefore, all information can
be accessed directly from the front-page without nav-
igating through complex menus. Users can select one
of sixteen categories. Each category contains one or
more subcategories, which contain one or more key
contents. By clicking the key contents the user arrives
at a text page where the first subcategory of the cho-
sen key content is explained in an easy to understand
way. Users can select different subcategories and key
contents in a side-bar.
The explanation starts with the most general con-
cepts and expands on them or delivers additional con-
text. Furthermore, references to the explained legisla-
tion are provided.
3.3 Parallel Corpus
To understand what differentiates easy law from nor-
mal law the legal information in the subcategories of
the key facts (further referred to as simple articles)
provided on the Easy-Law website is aligned with one
or more corresponding sentences in the statutes pro-
vided by the E-Law website. More precisely: given
a list of simple articles S
, S
, S
, ..., S
that consists
of stand alone legal information s
, s
, s
, ..., s
and a list of normal acts A
, A
, A
, ..., A
of sentences a
, a
, a
, ..., a
y, p
. A mapping of the
, s
, s
, ..., s
, a
, a
, ..., a
y, p
, a
, a
, ..., a
is generated, such that the information con-
tained on both sides of the mapping is the same.
n, x, m, o, y, p, z, q indicate control variables. To create
this mapping the following pipeline is executed: iden-
tification of stand-alone information in Easy-Law, ref-
erencing to corresponding E-Law act and extraction
of the referenced sentence(s) from the E-Law act.
Every paragraph in the key facts document of the
Easy-Law website that contains a reference to a legal
act is considered a stand-alone piece of legal informa-
tion. The reference is extracted with a simple regex/-
pattern search approach that searches the text for all
mentions of acts.
It is not possible to directly map the references
of the Easy-Law website to legislation on the E-Law
website, because of different providers and different
translations of the legislation names. Thus, BERT
word embeddings are used to find a match with a min-
imal cosine distance between the act names.
The reference from the Easy-Law is mapped to
the full act from the E-Law. This means, that the
E-Law side of the mapping can contain more infor-
mation than the Easy-Law side. However, to analyse
the textual differences between easy law text and nor-
mal law text this additional information on the normal
side is negligible.
717 simple articles are mapped to 2,183 structured
acts from the E-Law website leading to a parallel cor-
pus of 922 aligned samples.
To understand the textual difference between easy and
normal English Korean legislation both sides of the
parallel corpus are analysed. At first simple count
based analysis are performed, in a second step read-
ing formulas introduced in chapter 2.3 are used and
thirdly structural features are examined. In the end
the results are discussed.
4.1 Count Based Analysis
For each sample the following count based metrics are
number of syllables
number of words
(excluding punctuation)
number of sentences
number of characters
(including punctuation)
number of letters
(excluding punctuation)
number of polysyllables
(words with a syllable count greater or equal to
number of monosyllables
Resulting in the mean values shown in table 1. The
scores of simple law are always lower than the scores
of normal law. Interestingly, simple law uses on av-
erage about half of the measure that is used for nor-
mal law. Polysyllables are generally scarce which is a
characteristic of the English language.
However, these results have to be interpreted
as a trend. As mentioned in subsection 3.3 the
normal side of the parallel corpus can contain more
information and therefore more sentences.
ICAART 2022 - 14th International Conference on Agents and Artificial Intelligence
Table 1: Count based analysis of Korean legislation, all val-
ues represent the mean over all samples.
simple normal
syllables 139 271
words 81 164
sentences 1 3
characters 424 849
letters 411 812
polysyllables 17 30
monosyllables 48 100
4.2 Readability Scores
The following readability scores are computed on
each sample of the parallel corpus, using the python
library textstats
Flesch Reading Ease
The higher the score the more easy a text is to
read, the highest possible score is 121.22, negative
values are valid.
Dale Chall Formula
The higher the score the more complicated a text
is to read. Values above 9.0 indicate that readers
need at least a college education to understand the
FOG Index
Grade based score, that indicated which level of
school education a reader needs to understand the
text. E. g. a score of 9.3 indicated that readers
with a ninth-grade education could understand the
Flesch-Kincaid Grade Level Index
Grade based score, that indicated which level of
school education a reader needs to understand the
text. E. g. a score of 9.3 indicated that readers
with a ninth-grade education could understand the
The SMOG grading score is excluded, because it
is normed for text sequences of thirty sentences. As
discussed in subsection 4.1 the samples in the normal
part of the parallel corpus average about three sen-
Table 2 shows the result of the calculation.
According to readability scores simple law is harder
to read than normal law. Additionally, all scores
indicate that both kinds of law are hard to read.
4.3 Structural Features
Two kinds of structural features are examined for each
kind of law: the grammatical structure, represented as
Table 2: Calculation of readability scores for Korean legis-
lation, all values represent the mean over all samples.
simple normal
Flesch Reading Ease -3.36 6.82
Dale Chall Formula 12.06 11.69
FOG Index 31.73 29.27
Flesch-Kincaid Grade Level
29.69 27.53
pares tree expansion at a depth of level three and the
number of references to other articles.
Kauchak et al. (Kauchak et al., 2017) discuss that
parse trees at level two are too generic for in-depth
analysis of the grammatical structure because only
one expansion is done. This first expansion mostly
consists of a noun phrase in combination with a verb
phrase. Expansions after the third level become very
specific to the sentence at hand, complicating gener-
alisations over the texts. Table 3 shows the ve most
frequent grammatical structures in simple and normal.
The following tags are used:
DT - determiner
IN - preposition/complementiser
LST - list item marker
MD - modal
NN - singular noun
NP - noun phrase
PP - prepositional complement
S - sentence
SBAR - subordinate clause
VBN - past participle
VBZ - 3rd person singular present tense verb
VP - verb phrase
WHNP - wh-noun phrase
Both, normal and simple law sentences, mostly
follow the grammatical structure of S [NP [NP PP]
VP [MD VP]]. An example of this structure is “[(An
individual [NP]) (under influence [PP]) (may [MD])
(not drive [VP]).]”. In the second rank grammatically
structures begin to drift apart. Normal law favours
noun phrases without whole sentence constructs in
the second rank and later on rank four lists of singular
nouns. Simple law mostly adheres to full sentences
with noun phrases followed by verb phrases and
favours modals verbs in verb phrases. An in-depth
analysis of modal verbs shows that 69% of simple
sentences use modal verbs and only 21% of normal
sentences. In third rank, easy law also abandons the
conventional sentence construct in favour of a noun
How to Simplify Law Automatically? A Study on South Korean Legislation and Its Simplified Version
Table 3: Five most frequent grammatical structures of sim-
ple and normal law.
rank simple normal
01 S [NP [NP PP] VP
[MD VP]]
[MD VP]]
02 S [NP [NP SBAR]
03 NP [NP PP [IN NP]] S [VBN PP [IN NP]]
04 S [NP [NP VP] VP
[MD VP]]
05 S [NP [DT NN] VP
[MD VP]]
Going one level deeper into the parse tree enables
the detection of passive voice. Normal law uses pas-
sive voice in 43% of the parsed sentences and simple
law only 27%.
Law often references other parts of the law in one
article. The frequency of these references is an in-
teresting indicator of the readability of the text. Two
percent of simple law and twelve percent of normal
law reference to other articles. Though, if normal law
references other articles it references multiple ones
whereas simple law only references a few.
4.4 Discussion
Simple law tends to use fewer words and characters to
explain concepts, indicating a more direct communi-
cation style. The use of readability formulas does not
lead to useful results. These metrics mostly try to pre-
dict the school education, in other words grade level,
a reader needs to understand the text, but legislation
addresses complex topics that are above the knowl-
edge of an average school child. These readability
formulas perform well on average adult reading ma-
terial. However, to perform well on legal text these
formulas need domain-specific adjustments. These
adjustments have to be determined by linguistic ex-
perts. The structural analysis indicates that increased
usage of modal verbs and full sentences as well as the
reduced usage of passive voice increase the readabil-
ity of legal text. Furthermore, excessive referencing
to other acts decreases the readability of a law.
Therefore, readability measures that fit legal text
need to focus on shortness of text, while using com-
plete sentences and favouring modal words. More-
over, references in stead of direct explanations should
be penalised.
Automatic Text Simplification (TS) offers the op-
portunity to make the legislative text more accessi-
ble. Manually reformulating laws, regulations and an-
nouncements is a tedious and labour intensive task.
This chapter investigates how two state of the art TS
models perform on legal text by using the parallel cor-
pus described in subsection 3.3.
Both models are used in the pre-trained versions.
The easy law part of the corpus is used as ground truth
for evaluating the results.
5.1 Models
LSBert (Qiang et al., 2021) specialises in lexical sim-
plification. At first complex words are identified and
a list of possible replacements is generated. This
list is then ranked and the most suitable replacement
chosen. The automatic simplification is conducted
with the pre-trained model published by Qiang on
. Due to the sequential lookup, the model
performs and limited resources it was only possible
to generate simplifications for 1500 lines of text of
the normal law part of the parallel corpus.
The second model ACCESS in combination with
MUSS (Martin et al., 2020a) is more sophisticated
and paraphrases the input instead of just exchanging
words. For this experiment, the pre-trained version
provided on GitHub
by Facebook research is used.
For comparability reasons, the same 1500 lines of the
normal law of the parallel corpus are simplified.
5.2 Evaluation and Discussion
As discussed in subsection 4.4, conventional readabil-
ity formulas are insufficient to express the readability
of simplified law. Therefore, the evaluation concen-
trates on count-based measures as shown in table 4.
Overall both models seem to improve the readabil-
ity of the text, even slightly outperforming the base-
line of the simple law. As a lexical simplification
model LSBert is able to reduce the mean character,
letter and polysyllable count, ACCESS in combina-
tion with MUSS uses on average fewer words and
therefore syllables. Both models quantitatively re-
duce the complexity of the normal law texts.
Just reducing the complexity of words used in the
law could be misleading and change the meaning of
9 accessed on
10 accessed on
ICAART 2022 - 14th International Conference on Agents and Artificial Intelligence
Table 4: Comparison of count based measures for normal
law, simple law and the simplifications with LSBert and
ACCESS in combination with MUSS. All values depict the
mean over all lines.
normal simple LSBert ACCESS
syllable 56 53 52 48
words 34 31 32 30
sentence 1 1 1 1
char 173 161 147 150
letter 167 158 142 145
6 6 4 5
21 19 21 19
the law. Therefore, samples of the simplification re-
sults are explored and compared to the original law.
The main problems of both systems can be expressed
in the following two samples.
First article 5 of the civil act:
A minor shall obtain the consent of both parents
in order to marry. If one parent is unable to exer-
cise the right of consent, the minor shall obtain the
consent of the other parent, and if neither parent
is able to exercise the right of consent, the minor
shall obtain the consent of his or her guardian of
In case a minor or an adult ward marries, he/she
shall go through the following procedures for con-
sent. A minor (under the age of 19) shall obtain
the consent of both parents in order to marry. If
one parent is unable to exercise the right of con-
sent, the minor shall obtain the consent of the
other parent, and if neither parent is able to ex-
ercise the right of consent, the minor shall obtain
the consent of his/her guardian of minor.
A child will receive the permission of both parents
in order to meet. If one parent is unable to use the
right of permission, the child must get the permis-
sion of the other parent, and if neither parent is
able to use the right of permission, the child will
get the permission of his or her father of major.
ACCESS in combination with MUSS:
A minor must get the consent of both parents in
order to marry. If one parent is not able to give
consent, the minor must get the consent of the
other parent. If neither parent is able to give con-
sent, the minor must get the consent of the minor’s
The simplification shows shortcomings of the LS-
Bert model. The word minor is simplified to child and
marry to meet, changing the expressed meaning of
the text significantly. ACCESS in combination with
MUSS is able to simplify the text without compro-
mising the actual content.
Second article 633 of the civil act:
Rent shall be paid at the end of each month in the
case of a movable, a building, or a building site,
and at the end of each year in the case of any other
land: Provided, That in the case of a leased ob-
ject which has a harvest season, rent shall be paid,
without delay, upon the close of such a season.
If the lessee and the lessor have not determined
the timing to pay rent, rent shall be paid at the end
of each month.
it will be paid at the end of each month in the case
of a fixed, a building, or a building site, and at
the end of each year in the case of any other land:
Provided, That in the case of a used subject which
has a crop year, rent will be paid, without cause,
upon the close of such a season.
ACCESS in combination with MUSS:
Rent shall be paid at the end of each month in the
case of a movable object, a building or a building
site, and at the end of each year in the case of
any other land. The rent shall be paid, without
delay, upon the close of such season in the case of
a leased object.
ACCESS in combination with MUSS fails to re-
tain the complete meaning of the original text. The
model excludes the word harvest and, therefore, an
important part of the information. LSBert exchanges
the word harvest with the word crop. However, har-
vest season is an established concept describing a spe-
cific time of year. The crop season might refer to
the season of a specific crop that does not necessar-
ily span the same time period. Both models are not
able to do the complete paraphrase the baseline, done
by a human professional, suggests for this sample.
Both examples show that simple paraphrasing is
insufficient for legal text, which relies on established
concepts. If words are simplified with no regard of
their domain the content of the text can change, ren-
dering it unsuitable to explain the legislation to a
broader audience. The results of both models can be
improved by adding context-awareness.
How to Simplify Law Automatically? A Study on South Korean Legislation and Its Simplified Version
We examined the English translation of South Korean
legislation and its official simplification and produced
a parallel corpus by aligning both sources. Subse-
quently, we explored the parallel corpus and inves-
tigated how the normal legalisation differs from the
simple one. We concluded that simple legislation gen-
erally uses fewer and shorter sentences. Furthermore,
complete sentences, fewer passive voice and modal
verbs are favoured in simple law. Common Read-
ability measures lead to insufficient results and were
deemed unusable for legal texts. State of the art Text
Simplification models were able to quantitatively re-
duce the complexity of the normal legal text. How-
ever, the models had problems retaining all informa-
tion when used on the normal legal text in our paral-
lel corpus. Awareness to the domain of the words the
models paraphrase would improve the results.
This paper is based on the master thesis Automatic
English Text Simplification for Statutes” by Akshaya
Al-Thanyyan, S. S. and Azmi, A. M. (2021). Auto-
mated text simplification. ACM Computing Surveys,
Chandrasekar, R. and Srinivas, B. (1997). Automatic induc-
tion of rules for text simplification. Knowledge-Based
Systems, 10(3):183–190.
Coster, W. and Kauchak, D. (2011). Simple english
wikipedia: a new text simplification task. In Proceed-
ings of the 49th Annual Meeting of the Association for
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