txt/2107.03444.txt

Keep it Simple: Unsupervised Simplification of Multi-Paragraph Text
Philippe Laban
UC BerkeleyTobias Schnabel
MicrosoftPaul N. Bennett
MicrosoftMarti A. Hearst
UC Berkeley
Abstract
This work presents Keep it Simple (KiS), a
new approach to unsupervised text simplifica-
tion which learns to balance a reward across
three properties: fluency, salience and simplic-
ity. We train the model with a novel algorithm
to optimize the reward ( k-SCST), in which
the model proposes several candidate simpli-
fications, computes each candidate’s reward,
and encourages candidates that outperform the
mean reward. Finally, we propose a realis-
tic text comprehension task as an evaluation
method for text simplification. When tested on
the English news domain, the KiS model out-
performs strong supervised baselines by more
than 4 SARI points, and can help people com-
plete a comprehension task an average of 18%
faster while retaining accuracy, when com-
pared to the original text.
1 Introduction
The main objective of text simplification is to make
a complex text accessible to a wide audience by
increasing its readability. In contrast with text sum-
marization – in which key content is selected to
remain in the summary and other content is elided
– in text simplification, ideally all relevant content
is preserved.
We propose that text simplification algorithms
need to balance three properties: (1) fluency : the
simplified text should use well-formed English sen-
tences, (2) salience : the simplified text should relay
the same information as the original, and (3) sim-
plicity : the simplified text should be syntactically
and lexically simpler than the original.
Figure 1 provides intuition for the necessity of
each of the three properties. It shows the origi-
nal text and the output of the full proposed model
compared to three reduced versions:
Author emails: fphillab,hearst [email protected],
fTobias.Schnabel,Paul.N.Bennett [email protected]
Original: NASA's Curiosity rover just celebrated a major
milestone — 3,000 days on the surface of Mars. T o mark the
occasion, the space agency has released a stunning new
panorama of the red planet, captured by the rover .
Model Full: NASA's Curiosity rover has now passed 3,000
days of travel on the surface of Mars. T o mark the milestone,
the space agency released a huge panorama of Mars, as
seen by the rover .
Model No Fluency:  NASA's Curiosity rover . celebrated. A
major milestone — 3,000 days on. The of.. T o mark. The
space agency has. a stunning new panorama.. red planet.
captured by . The rover . However
Model No Salience: NASA's Curiosity rover just celebrated a
major milestone. The space agency has released a stunning
new panoramic of the red planet, captured by the team. It
was by the rover's panoramic camera.
Model No Simplicity: NASA's Curiosity rover has celebrated
a major milestone, 3,000 days on the ground of Mars. T o
mark the occasion, the space agency has unveiled a stunning
new panoramic view of the red planet, captured by the rover .Figure 1: Motivating example for the KiS method,
based on a CBS article (Lewis, 2021). We optimize a
three-component reward: fluency, salience and simplic-
ity. We show model outputs when trained with all three
components, and with a missing component.
Without Fluency , the generator has no incen-
tive to generate full sentences, and learns it can
boost the simplicity score by generating short
phrases with excessive punctuation.
Without Salience , the generator does not gain
by covering facts in the original text, and can im-
prove the simplicity score by learning to remove
facts (e.g., not mentioning planet Mars by name).
Without Simplicity , the generator is not guided
to favor syntactically and lexically simpler re-
writes. In Figure 1, Model No Simplicity is in fact
more complex than the original according to read-
ability measures.
As we show in the related work section (Sec-
tion 2), there are no high-quality, large datasets
publicly released for text simplification. In this
work, we build on recent progress of reinforcement
learning (RL)-based training of text generators: wearXiv:2107.03444v1  [cs.CL]  7 Jul 2021formulate a reference-free reward for text simplifi-
cation and directly optimize it, circumventing the
need for aligned data.
Our main contribution is the Keep it Simple
(KiS) procedure, a novel unsupervised method for
text simplification. Applied to the English news do-
main, KiS outperforms several supervised models
on common simplification metrics such as SARI
(Xu et al., 2016) and the Flesch-Kincaid Grade
Level (Kincaid et al., 1975).
A second contribution is a new algorithm for RL-
based training of text generators, k-SCST, which
is an extension of Self-Critical Sequence Training
(Rennie et al., 2017). For each input, we generate
ksampled outputs (vs. 2 in SCST), and use the
mean population reward as a baseline. We show in
Section 4 that in our domain, k-SCST outperforms
models trained with SCST.
A third contribution is a novel evaluation method
for text simplification. Based on the assumption
that simplified text should enable faster reading
with better understanding, we propose a realistic
Text Comprehension task. We show that people
reading texts simplified by KiS are able to complete
comprehension tasks faster than comparison texts.
Another departure from previous work is that we
work with paragraphs as units of text. Most work
in text simplification is done at the sentence level,
despite work such as Zhong et al. (2020) showing
that common simplification phenomena occur at
the level of the paragraph, (e.g., the deletion, inser-
tion or re-ordering of full sentences). Specifically,
we train our models to simplify full paragraphs,
and evaluate our models in a human evaluation on
short documents (i.e., 3-4 paragraphs).
Through rigorous empirical evaluation, we
demonstrate the strong performance of our ap-
proach; automated results show that this unsuper-
vised approach is able to outperform strong su-
pervised models by 4 SARI points or more. We
publicly released the code and model checkpoints1.
2 Related Work
Simplification Datasets. Early datasets were first
based on Simple Wikipedia2: WikiSmall (Zhu
et al., 2010), later expanded into WikiLarge (Zhang
and Lapata, 2017). Xu et al. (2015) show there are
quality concerns with Simple Wikipedia datasets,
1https://github.com/tingofurro/keep_
it_simple
2https://simple.wikipedia.org/and propose Newsela3as a replacement. Newsela
is a project led by educators re-writing news ar-
ticles targeting different school grade levels. We
view Newsela as the gold-standard for our work,
and use the public Newsela release of 1,911 groups
of articles to design and evaluate our work. Us-
ing a coarse paragraph alignment algorithm, we
extract 40,000 paired simple/complex paragraphs
targeting a separation of 4 grade levels. We call
this dataset the paired Newsela dataset , which we
use for analysis and baseline training.
Seq2Seq for Simplification . Text simplifica-
tion is most commonly framed as a sequence-to-
sequence (seq2seq) task, leveraging model archi-
tectures of other seq2seq tasks, such as natural ma-
chine translation (Zhu et al., 2010; Wubben et al.,
2012). Martin et al. (2020) introduce ACCESS, a
finetuned Transformer model that achieves state-
of-the-art performance on WikiLarge. ACCESS
can customize simplifications on parameters such
as compression rate and paraphrase amount. We
directly compare our approach to ACCESS.
Data availability remains one of the main lim-
itations to seq2seq-based text simplification. We
side-step this issue entirely by working with unsu-
pervised data, only requiring a small dataset with
coarse-level alignments for calibration.
Lexical Simplification focuses on the substi-
tution of single words or phrases with simpler
equivalents, with diverse approaches using lexical
databases such as WordNet (Thomas and Anderson,
2012), to using contextualized word vectors (Qiang
et al., 2020). These methods tend to be limited, as
they do not consider syntactic complexity, and have
no direct way of modeling deletions and insertions.
We incorporate a lexical score ( LScore ) as one of
the rewards in our simplicity component.
Text-edit for Simplification . Recent work
(Dong et al., 2019; Stahlberg and Kumar, 2020)
has modeled text simplification as a text-edit task,
learning sequences of word-edits that transform the
input into the output. Text editing offers explain-
ability, at the cost of added model complexity. We
find that without explicitly representing edits, the
KiS model easily learns to copy (using attention
heads) and deviate from the original text. Outputs
can be post-processed into edits, if desired.
Unsupervised Simplification has mostly been
limited to lexical simplification. Recently Surya
et al. (2019) (Unsup NTS) proposed a system that
3https://newsela.com/can perform both lexical and syntactic simplifica-
tion, with a joint encoder, and two decoders (simple
and complex). We directly compare our unsuper-
vised approach to Unsup NTS.
RL for Simplification . Prior work (Zhang and
Lapata, 2017; Guo et al., 2018) used Reinforce-
ment Learning (RL)-based simplification. How-
ever, in both cases, components of the reward or
training procedure involved reference simplifica-
tions, requiring an aligned dataset. By designing
a reference-free reward, we are able to train our
model with RL without supervision.
Evaluation of Simplification . This usually falls
into two categories: automatic offline evaluation,
and human evaluation. Automatic evaluations usu-
ally involve using n-gram overlap calculations such
as BLEU (Papineni et al., 2002) and SARI (Xu
et al., 2016)). SARI was shown to correlate better
with human judgements of simplicity than BLEU,
and it has since become a standard (Zhang and Lap-
ata, 2017; Surya et al., 2019; Martin et al., 2020). In
our experiments, we report both SARI and BLEU.
Human evaluation is typically done in an intrin-
sicway – e.g., by directly rating factors like fluency,
simplicity and relevance of model outputs (Surya
et al., 2019; Wubben et al., 2012). In this work,
we propose an extrinsic, task-based protocol. In
our comprehension study, we directly measure how
much simplified texts can help a human reader an-
swer questions more efficiently. The closest to our
evaluation design is that of Angrosh et al. (2014)
with the important difference that we require par-
ticipants to resubmit after erroneous answers. In
pilot studies, we found this step to be crucial for
high-quality responses.
3 KiS Components
In KiS, we approach unsupervised simplification as
a (non-differentiable) reward maximization prob-
lem. As shown in Figure 2, there are four compo-
nents to the reward: simplicity, fluency, salience
and guardrails which are jointly optimized. This
is essential to avoid trivial solutions that only con-
sider subsets. We therefore use the product of all
components as the total reward, because the prod-
uct is sensitive to the sharp decrease of a single
component. For example, the triggering of a single
guardrail leads to the zeroing of the total reward.
Each component is normalized to the [0;1]range.
     Generator
           SalienceOriginal
Text
       Simplicity
ScoreOptimization
       Fluency
           Guardrails
 Simplified
TextFigure 2: Keep it Simple is an unsupervised training
procedure for text simplification. The text generator
(GPT-2) produces candidate simplifications, scored ac-
cording to fluency ,simplicity ,salience .Guardrails en-
force the model does not learn high-scoring shortcuts.
def S_Score(original,simple):
Fstart = fkgl(original)
tgt = target_delta(Fstart)
Fend = fkgl(simple)
D = Fend-Fstart
return clip(1-((D-tgt)/tgt),0,1)
def target_delta(Fstart):
# Line-fitted from analysis
ifFstart < 4.0:
return 0.1
ifFstart < 12:
return 0.5*Fstart-1.9
return 0.8*Fstart-5.6
Figure 3: SScore algorithm. fkgl computes the
Flesch-Kincaid grade level.
3.1 Simplicity
The simplicity score should establish whether the
generator’s output uses simpler language than the
original text. We follow prior work (Ferr ´es et al.,
2016) and organize our score into a syntactic score
SScore , and a lexical score LScore . Syntactic sim-
plification focuses on reducing the complexity of a
sentence, for example by reducing the number of
words in a clause, or reducing distant dependencies.
In lexical simplification, the objective is to replace
complex phrases with simpler synonyms. To pro-
duce a single simplicity score, we take the product
ofSScore andLScore (both in [0;1]).
3.1.1 Syntactic Simplicity: SScore
We measure syntactic complexity via the Flesch-
Kincaid grade level (FKGL) as it is easy to compute
and maps to a grade-level which also corresponds
to the scale used by Newsela. Other readability met-
rics such as Dale-Chall formula (Dale and Chall,
1948), or the Gunning-Fog index (Gunning, 1969)
could be used, and future work could examine the
effect of choosing one readability metric over the5.0 7.5 10.0 12.5 15.0 17.5
FKGL of original paragraph5
0510FKGL in Newsela rewrite
Linear approximationFigure 4: Analysis (Kernel Density Estimate plot)
of change in Flesch-Kincaid Grade Level in the
paired Newsela dataset. Most simple paragraphs have
lower FKGL than the original paragraphs (positive

FKGL ). When the original paragraph’s FKGL is
higher (x-axis), the change in FKGL tends to be larger
(y-axis). We fit a linear approximation, which we use
to compute the Sscore .
other. Another viable option is the Lexile score
(Smith et al., 2016), however, because its imple-
mentation is not publicly released, we cannot use it
during training and we report it only for evaluation
(done manually on the Lexile Hub4).
Figure 3 shows the SScore algorithm. We com-
pute the original paragraph’s FKGL ( FStart ),
used to compute a target FKGL ( tgt). The score
is a linear ramp measuring how close the achieved
FKGL ( Fend ) is to the target, clipped to [0;1].
In the initial design, the target drop was a con-
stant: 4 grade levels, independent of FStart .
However, analysis on the paired Newsela corpus
revealed that the target FKGL should depend on
the initial FKGL. This makes sense intuitively: an
already syntactically simple paragraph should not
require further simplification, while more complex
paragraphs require more simplification. Figure 4
shows the positive correlation between the original
paragraph’s FKGL and the drop of FKGL in the
simplified text. We fit a piece-wise linear function
to calculate the target FKGL drop from the initial
paragraph.
3.1.2 Lexical Simplicity: LScore
Lexical simplicity focuses on whether words in
the input paragraph ( W1) are more complex than
ones in the output paragraph ( W2). We rely on the
observation that word frequency and difficulty are
correlated (Breland, 1996), and use word frequency
in a large corpus of text (Brysbaert and New, 2009)
to determine simplicity.
4https://hub.lexile.comBecause word frequency follows a Zipf power
law, we use Speer et al. (2018)’s log normaliza-
tion, adjusting the frequency on a [0;8]range, with
words at 0 being non-existent in the corpus, and 8
for most common words. As an example, the word
vigorous has a frequency of 3:54, while its more
common synonym strong obtains 5:23.
We compute the average Zipf frequency of the
set of inserted words ( Z(W2W1)), and the set
of deleted words ( Z(W1W2)). The difference

Z(W1;W2) =Z(W2W1)Z(W1W2)(1)
should be positive. Analysis of the paired Newsela
corpus reveals that 91% of pairs have a positive

Z(W1;W2), with a median value of 0:4. We use
this median as the target Zipf shift in the LScore ,
and use a ramp shape similar to the SScore , clipped
between 0 and 1 (denoted as [
]+):
LScore(W1;W2) ="
1j
Z(W1;W2)0:4j
0:4#+
(2)
3.2 Fluency
We use two sub-components for the fluency com-
ponent: a pre-trained language-model, and a dis-
criminator trained dynamically with the generator.
3.2.1 Language-Model Fluency
Language models assign a probability to a sequence
of words. This probability is often used to measure
fluency of generated text (Kann et al., 2018; Salazar
et al., 2020). The KiS fluency score is based on
a language model in a way similar way to Laban
et al. (2020). The language model is used to ob-
tain a likelihood of the original paragraph ( LM(p))
and of the generated output LM(q). We use av-
erage log-likelihood, for numerical stability. The
language model fluency score is then:
LMScore(p;q) =h
1LM(p)LM(q)
i+
(3)
is a tunable hyper-parameter. If the LM(q)is
lower thanLM(p)byor more,LMScore(p;q) =
0. IfLM(q)is above or equal to LM(p), then
LMScore(p;q) = 1 , and otherwise, it is a linear
interpolation.
We set= 1:3as it is the value for which
thepaired Newsela dataset achieves an average
LMScore of 0.9.3.2.2 Discriminator Fluency
TheLMScore is static and deterministic, which can
be limiting, as the generator can learn during train-
ing how to adapt and exploit flaws in the language-
model (e.g., learning to alter capitalization).
Inspired from the Generative Adversarial Net-
work (GAN) framework (Goodfellow et al., 2014),
we create a dynamic discriminator, trained in con-
junction with the generator, dynamically adapting
the fluency score during training.
Specifically, we use a RoBERTa model (Liu
et al., 2019) as the basis for the discriminator, a clas-
sifier with two labels: 1 for authentic paragraphs,
and 0 for generator outputs.
As the generator produces outputs, they are as-
signed a label of 0 and added to a training buffer ,
while the original paragraphs are assigned a label
of 1 and added to the training buffer as well.
Once the training buffer reaches a size of 2,000
samples, the discriminator is trained, using 90% of
the training buffer. We train the discriminator for
5 epochs (details of training are in Appendix A.1).
At the end of each epoch, we checkpoint the dis-
criminator model. We compare the 5 checkpoints
in terms of F-1 performance on the remaining 10%
of the training buffer, and keep the best checkpoint
as the new discriminator.
The discriminator’s probability that a paragraph
(q) is authentic is the discriminator score:
DScore(q) =pdisc(Y= 1jX=q) (4)
As with GANs, there is an equilibrium between
the generator attempting to maximize the proba-
bility of generating real outputs (“fooling” the dis-
criminator), and the discriminator succeeding at
distinguishing generated and authentic texts.
3.3 Salience
For the salience component, we use the coverage
model introduced in the summary loop (Laban
et al., 2020) for the domain of text summarization,
and adapt it to the simplification domain.
The coverage model is a Transformer-based
model trained to look at generated text and answer
fill-in-the-blank questions about the original text.
The score is based on model accuracy at filling in
the blanks: the more is filled in, the more relevant
the generated content is, and the higher the score.
A key element of the coverage model is its mask-
ing procedure, which decides which words to mask.
In the summary loop, a limited number of extractedkeywords (up to 15 words) are masked. By contrast,
for simplification, we mask all non-stop words,
amounting to a masking rate of about 40%.
This change reflects a difference in expectation
between summarization and simplification: in sum-
marization, only key components are expected to
be recovered from a summary, whereas in simpli-
fication most of the original paragraph should be
recoverable. Coverage ranges in [0;1], and refer-
ence simplifications in the paired Newsela corpus
obtain an average score of 0.76, confirming that
manual simplification can achieve high coverage.
3.4 Guardrails
We use guardrails as simple pattern-based scores to
avoid common pathological generation problems
that we observed. Unlike the main components,
guardrails are binary, giving a score of 1 (pass) un-
less they trigger (score of 0). We use two guardrails:
brevity and inaccuracy.
3.4.1 Brevity guardrail
The brevity guardrail ensures the length of gen-
erated paragraph ( L2) falls in a range around the
original paragraph’s length ( L1). We compute a
compression ratio: C=L2=L1. IfCminC
Cmax, the guardrail passes, otherwise it triggers.
We set [Cmin;Cmax] = [0:6;1:5], because these
values ensure the guardrail is not triggered on 98%
of the paired Newsela dataset; this can be adapted
depending on the application.
3.4.2 Inaccuracy guardrail
Modern text generation models are known to hallu-
cinate facts (Huang et al., 2020), which has led the
community to create models to detect and correct
hallucinations (Cao et al., 2020; Zhang et al., 2020;
Wang et al., 2020).
We propose a light-weight inaccuracy detector
as a guardrail. We use a Named Entity Recognition
(NER) model (Honnibal et al., 2020) to extract
entities present in the original paragraph ( E1) and
the model’s output ( E2). We trigger the guardrail
if an entity present in E2is not inE1.
Even though human writers can successfully in-
troduce new entities without creating inaccuracies
(e.g., replacing the city La Paz with the country Bo-
livia), we find that text generators predominantly
introduce inaccuracies with novel entities. This
simple heuristic can eventually be replaced once
inaccuracy detection technology matures.2 4 6 8 10 12
Hours of Training105
104
103
102
Total Score8-SCST
6-SCST
4-SCST
SCSTFigure 5: Training KiS models comparing SCST
withk-SCST. We try 4, 6 and 8 as values for k. In-
creasing k improves performance and stability.
4 KiS Training
Rennie et al. (2017) introduced Self-Critical Se-
quence Training (SCST) as an effective algorithm
for reward-based training of text generators, suc-
cessfully applying it to image captioning. The effi-
cacy of SCST was later confirmed on other text gen-
eration tasks such as question generation (Zhang
and Bansal, 2019), and summarization (Celikyil-
maz et al., 2018; Laban et al., 2020). In SCST, a
probabilistic model is used to generate two distinct
candidates: CS, a candidate constructed by sam-
pling the word distribution at each step, and ^C, by
taking the argmax of the word distribution at each
step. Each candidate is scored, obtaining rewards
ofRSand^R, respectively, and the loss is:
L= (^RRS)NX
i=0logp(wS
ijwS
1:::wS
i1;P)(5)
wherep(wS
ij:::)represents the probability of the
i-th word conditioned on previously generated sam-
pled sequence according to the model, P is the input
paragraph, and N the number of words in the gen-
erated sequence. Intuitively, minimizing this loss
increases the likelihood of the sampled sequence if
RS>^R, and decreases it otherwise, both increas-
ing the expected total reward.
One limitation in SCST occurs when the two
sequences achieve comparable rewards ( RS'^R):
the loss nears zero, and the model has little to learn,
wasting a training sample. In our experiments with
SCST, this can occur with 30% of samples.
We propose an extension of SCST, which we
callk-SCST. We generate ksampled candidates
(k > 2), compute the rewards of each candidate
RS1;:::;RSk, as well as the mean reward achievedby this sampled population:RS= (RS1+:::+
RSk)=k, which we use as the baseline, instead of
^R. The lossLbecomes:
L=kX
j=1(RSRSj)NX
i=0logp(wSj
ijwSj
1:::wSj
i1;P)
(6)
We use a GPT2-medium for the generator, ini-
tialized with the released pre-trained checkpoint.
Experimental details such as data and optimizer
used are provided in Appendix A.1.
In Figure 5, we show results of a direct compar-
ison of SCST ( k= 2) withk-SCST varying kin
f4;6;8g, while keeping other components of the
training fixed. Because of the variance involved in
RL training, we recorded six independent training
runs for each setting (for a total of 24 runs), and
plot the average reward across runs of a setting, as
well as the standard error of the mean (SEM).
We observe that increasing kleads to higher
average reward, and less variation in the reward.
In our setting, k-SCST boosts performance and
stabilizes training. We use k= 8in all final models,
as increasing kfurther is impractical due to GPU
memory limitations.
We believek-SCST’s advantage stems from two
factors: first, obtaining a better estimate of the
distribution of rewards by sampling more outputs,
second, by using the mean reward as the baseline,
saving on computation of a separate baseline gener-
ation. We believe k-SCST can also improve learn-
ing in other text generation applications and plan
to pursue this in future work.
5 Experiments
We present results experimentally validating the
KiS procedure for text simplification. We give re-
sults based on automatic metrics, on a novel human
comprehension task, and from an ablation study.
5.1 Models Compared
We compare the KiS Model to three strong super-
vised models, and an unsupervised approach.
ACCESS from (Martin et al., 2020), is a state-
of-the-art Transformer model trained on WikiLarge
(300,000 pairs of complex/simple sentences). This
model uses default parameters ( NBChar =0.95,
LevSim =0.75).
ACCESS90 is identical to ACCESS , with dif-
ferent parameters ( NBChar =0.90, LevSim =0.75),
reducing target compression from 95% to 90%,
matching the average compression rate in Newsela.Model SARI BLEU %FKGL %Lexile Comp. Cov.
Newsela - - 87 79 .918 .754
Finetune Baseline .470 .719 68 52 .903 .894
ACCESS Default .666 .649 86 63 .958 .805
ACCESS 90 .674 .644 93 64 .921 .789
Unsup NTS .677 .535 48 57 .753 .618
KiS Model .709 .526 100 72 .852 .640
Table 1: Automatic results on Newsela test-set. SARI
andBLEU are reference-based metrics. %FKGL and
%Lexile are percentages of model outputs lowering the
grade level. Comp. is the average compression ratio (#
words), and Cov. the output’s average coverage score.
Finetune Baseline is a GPT2-medium model
finetuned on the paired Newsela dataset . Large
pre-trained models often perform competitively in
low-resource environments, making this a strong
point of comparison.
Unsup NTS from (Surya et al., 2019) is an unsu-
pervised approach based on successively encoding
and denoising text using a GRU architecture.
Training details for the KiS Model and Finetune
Baseline are in Appendix A.1.
5.2 Automatic Results
We put aside 500 samples from the paired Newsela
dataset as a test set to compare models on auto-
matic metrics. We compare models on SARI and
BLEU, report the percentage when readability mea-
sures see an improvement in readability: %FKGL,
and %Lexile and compute the average compres-
sion rate (Comp.), and coverage (Cov.). Results are
summarized in Table 1.
The KiS model achieves the highest SARI score
by a margin of 0.04, even though it is an unsuper-
vised approach.
Finetune Baseline achieves the highest BLEU
and salience scores, but lowest SARI score. We
interpret this as showing the model takes the least
risk: high salience, with little simplification.
We observe that all models are able to increase
readability in terms of FKGL and Lexile compared
to original paragraphs. We note that for almost all
models, the percentage is lower for the Lexile mea-
sure than for FKGL, showing that an improvement
in Lexile score is more difficult to achieve than
FKGL. The KiS model achieves an increase in Lex-
ile readability 72% of the time, the closest figure
to 79% of the Newsela human-written reference.
We note that the perfect performance of KiS on
%FKGL could be explained by the fact that FKGL
is a part of a component being optimized ( SScore ),
however Lexile was not.In terms of compression, the KiS model com-
presses the second most, most likely hurting its
coverage. Adjusting the Brevity guardrail could
encourage the model to compress less. ACCESS90
has the compression rate closest to Newsela refer-
ences, but this only leads to a modest improvement
in SARI when compared to ACCESS.
Overall, the Newsela references achieve the
best percentage of Lexile readability improvement,
while outperforming the KiS model at coverage:
there is still a gap between human-written simplifi-
cations and model-generated ones.
5.3 Human Comprehension Study
We propose a human comprehension study to evalu-
ate the usefulness of simplification results. Simpli-
fied text should be easier to read than the original
text, while retaining accuracy and understanding.
We design a task to evaluate how well both manual
and automated simplifications achieve this objec-
tive. The main idea is to show readers a text and
ask them to answer multiple-choice questions, eval-
uating the texts based on time and retries needed to
select the correct answer.
5.3.1 Study Design
Five different versions of each document were
generated as stimuli: the original document, the
Newsela reference, and versions from the three
best-performing methods from the last section:
KiS, Finetune Baseline, and ACCESS. We did not
include Unsup NTS in our analysis, because of its
low performance on %FKGL and %Lexile metrics.
Associated with each document are five manually
generated multiple-choice questions, each with one
or more correct answers and one to four distractors.
The original and the Newsela texts were checked
manually by experimenters to ensure that all allow
for questions to be answered correctly. Crowd-
workers were shown four documents in succession,
in a between-participants design. Order of docu-
ment and stimuli type were randomized. Figure 6
shows two stimuli of a document (original and KiS)
along with the comprehension questions. (The en-
tire set of five stimuli can be found in Figure A2 in
the Appendix.)
After several rounds of pilot testing, we arrived
at the following design choices:
Document theme. We chose recent news arti-
cles involving complex themes (e.g., trajectory of
iceberg) as the source of documents. For news ar-
ticles, recency seems to engage participants, andORIGINAL [Lexile Grade 1 1] Each summer , libraries in St. Louis,
Missouri, host many  types of free camps — yoga, chess and even a
Harry Pot ter “Sorting Hat Camp.” In 2020, camp dreams seemed far-
fetched given the global coronavirus pandemic. That didn’t stop St.
Louis libraries, though.
Instead of canceling, they brought camp into kids’ homes. So children
who signed up for ukulele camp got a beginner ’s guidebook,
instructional DVD and an actual ukulele in the mail. It was all free. In
addition, camp sessions still occurred. Advisers met with kids using
virtual formats.
Joe Monahan, manager of youth services for the St. Louis  library
system, says that of the 70 camps originally scheduled, 54 were held
virtually .
Paula Langsam, a youth services manager at the soon-to- reopen
Martin Luther King Junior Memorial Library in W ashington, D.C., says,
“In a way , our work has changed a lot. W e didn’t used to do videos a
lot.”KIS MODEL [Lexile Grade 9] In the summer months, St. Louis
has many free classes for  kids, including yoga, chess and a Harry
Potter “Sorting Hat Camp.” In 2020, camp dreams again seemed
far-fetched  given the crisis. That didn’t stop St. Louis libraries,
though.
They brought camp in. So kids who signed up for ukulele camp got
a beginner ’s guidebook, a lesson DVD and a real ukulele in the
mailbox. It was all free. In addition, camp  sessions continued.
Advisers tried out a virtual format.
Joe Monahan, the manager of youth services for the St. Louis
library system, says that of the 70 camps originally scheduled, 54
were held mostly .
Paula Langsam, a youth services manager at the Martin Luther
King Junior library , says, “In a way , our work changed a lot. W e
didn’t do videos a lot.”
Who manages the St Louis library kids programs?
Joe Monahan , Paula Langsam, St. Louis Camp Leaders
Were any camps in St. Louis cancelled?
Yes, NoHow many camps were scheduled, how many were run?
54 and 70, 70 and 54 , 70 and 0, 54 and 0
How did the Ukulele camp meet?
In the park, Virtually , Did not meetWhat camps did the libraries host?
Yoga, Chess , Pottery , Ukulele
Figure 6: Example Task (from a Washington Post article (Kelati, 2020)) for the Comprehension Study. Shown
are two of five stimuli: original document (left), and KiS model output (right). Participants read a text and answered
comprehension questions (bottom). Average completion time was 160 seconds (original) and 136 seconds (KiS
model output).
technical terms increase the impact of simplifica-
tion.
Section length. We chose document length of
3-4 paragraphs (or 200 words), and five compre-
hension questions. Document length should not be
too W (makes some questions trivial), or too long
(adds a retrieval component to the task).
Selection of questions. Questions were gener-
ated via a GPT2 question generation model fine-
tuned on the NewsQA dataset (Trischler et al.,
2017). We select questions answerable by both
the original and Newsela references, attempting to
have both factoid (answer is entity) and reasoning
questions.
Re-submission until correct. When submitting
answers, participants received feedback on which
were incorrect, and were required to re-submit un-
til all answers were correct. This aligns the ob-
jective of the participant (i.e., finishing the task
rapidly), with the task’s objective (i.e., measuring
participant’s efficiency at understanding). This also
gives a way to discourage participants from “brute-
forcing” the task, re-submitting many combinations
until one works.
We note that some components of the study such
as the choice of document themes and the selection
of comprehension questions are elements that cre-
ate variability in the results. We release the models
used in the study, as well all generated texts that
were evaluated to enable follow-up research and to
aid reproducibility.Model Time (sec) # Subs. Comp. CASpeed
[Original 174.0 4.23 1.0 1.00
\Newsela 163.3 5.10 1.08 1.15
8ACCESS 188.5 6.69 0.96 0.88
9Finetune Baseline 161.0 8 4.70 0.97 1.04
rKiS Model 142.6[\84.108 0.87 1.06
Table 2: Results of the Human Comprehension
Study. We measure average completion time (Time),
number of submissions (#Subs.), compression ra-
tio (Comp.) and a compression-accounted speed-up
(CASpeed). Each text version is assigned a symbol
used to indicate statistical significance ( p<0:05).
5.3.2 Study Results
We ran the study on Mechanical Turk, accepting
crowd-workers with 1700+ completed tasks, and
an acceptance rate of 97%+. The study was active
for two weeks in December 2020, and remunerated
participants completing all four sections at a rate of
$10/hour. (Appendix A.2 shows crowd-worker in-
structions and the document/version distributions.)
When removing “brute-forced” submissions (10+
re-submissions), we are left with 244 submissions,
used for result analysis reported in Table 2, (A more
detailed results table is included in Appendix A.4.)
We measure two outcomes: question comple-
tion time (in seconds), and number of submissions
to correctness. We performed a Kruskal-Wallis
test (Kruskal and Wallis, 1952) with a Dunn post-
hoc test (Dunn, 1964) for statistical significance
between pairs of conditions.
In line with study objectives, simplified textshelp participants complete the task faster than read-
ing original texts, with three of the four simplified
versions leading to improvements in completion
times. Participants were fastest with KiS simpli-
fications (18% faster). The KiS model led to a
statistically significant speed-up compared to the
originals, Newsela references, and ACCESS sim-
plifications. ACCESS simplifications surprisingly
led to a non-significant slow-down, which we at-
tribute to a potential loss in fluency that might have
confused participants.
One important factor we consider is that shorter
passages (i.e., smaller compression) might lead to a
speed-up regardless of simplicity. We confirm this
by finding a small positive correlation between pas-
sage length and completion time of 0.09. We com-
pute a compression-adjusted speed-up (CASpeed )
ratio by: (1) computing the passage length of each
simplified version, (2) linearly extrapolating the ex-
pected completion time for this passage length for
original paragraphs, and (3) computing the ratio of
the extrapolation to the observed completion time.
IfCASpeed> 1, participants were faster than ex-
pected for the passage length. Newsela reference
paragraphs achieve the best CASpeed , followed by
the KiS model. This suggests that good simplifica-
tion can involve making texts longer.
5.4 Ablation Study
We train three ablated models, each missing a re-
ward component to gain understanding in the value
of each component of the KiS procedure.
Figure 1 gives a qualitative perspective on each
ablation. Without fluency, the generator learns to
generate incomplete sentences, without salience, it
omits important information, and without simplic-
ity, it can sometimes “complexify”.
We computed complete automatic results for the
ablated models, and find that each ablation leads to
a decrease on an evaluation metric, confirming that
all three components are necessary to generate high-
quality simplifications (details in Appendix A.5).
6 Limitations and Future Work
Improved Accuracy Scoring . The current
guardrail for inaccuracy is rudimentary; trained
models still generate non-factual simplifications.
Recent work in fact-checking for the summariza-
tion domain (Kryscinski et al., 2020; Li et al., 2018)
could be adapted to the simplification domain to
improve this.Inclusion of Supervised Signal . In this work,
we establish that text simplification can be ap-
proached in an unsupervised manner. In future
work, Keep it Simple could be used as a pre-
training strategy, or used jointly with supervised
training.
Reproducibility of Human Evaluation . Even
though we release the models, stimuli and compre-
hension questions used in the human evaluation,
some elements of the procedure introduce random-
ness. Participating crowd-workers differ in literacy
level which may have an effect on their perfor-
mance at the task (Alonzo et al., 2021).
New Settings, Domains and Languages . We
limited our experiments to the simplification of En-
glish news articles following prior work, but plan
to pursue other languages in the future. Similarly,
because Keep it Simple does not require labeled
data, it can be applied to new settings (e.g., rewrit-
ing to inverse the effects of simplification), or to
new domains (e.g., legal texts).
7 Conclusion
We have shown that text simplification can be ap-
proached in an unsupervised manner via KiS. By
optimizing a reward comprised of simplicity, flu-
ency and salience components, KiS is able to out-
perform strong supervised models on automatic
metrics (+0.04 in SARI). We propose a human
comprehension task to evaluate the usefulness of
simplification and show that simplifications tend to
lead to a measurable speed-up in task completion,
with KiS texts producing the best speed-up of 18%
on average. These are first steps for unsupervised
text simplification, and we suggest that future work
should focus on adapting the methodology to new
domains (i.e., legal), non-English languages, and
refining optimized rewards to take factuality into
account.
Acknowledgments
We would like to thank Katie Stasaski, Dongyeop
Kang, and the ACL reviewers for their helpful com-
ments, as well as Newsela for providing a version
of their simplified news corpus. This work was
supported by a Microsoft BAIR Commons grant as
well as a Microsoft Azure Sponsorship.References
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We present a method for text simplification and ver-
ify its performance on text from the news domain
in the English language. Even though we expect
the method to be adaptable to other domains and
languages, we have not verified this assumption
experimentally and limit our claims to the English
news domain.
When comparing to prior work (e.g., ACCESS
model), we obtained implementations directly from
the authors (through Github repositories) and pro-
duced results following the recommended setting,
with an objective to present prior work as a strong
comparison point.
For the human evaluation, we paid the annota-
tors above the minimum wage, and did not collect
any personal identifiable information. We selected
topics to avoid sensitive or political subjects and
had our protocols reviewed by the university’s IRB
committee (Protocol ID: 2018-07-11230). We re-
lied on a third party (Amazon Mechanical Turk) to
remunerate the crowd-workers.
A Appendices
A.1 Training Details
We detail the model architecture size, data, opti-
mizer of the models we train in the paper. All
models were trained using Pytorch and Hugging-
Face’s Transformers library5. We use the Apex6
library to enable half-precision training.
The KiS procedure was trained on a single
GPU, either an Nvidia V-100 (16Gb memory) or
a Quadro RTX 8000 (48 Gb memory). We ran a
total of around 200 experiments, with an average
run-time of one week.
Because the procedure is unsupervised, the
model was trained using a large unreleased cor-
pus of news articles, containing 7 million news
articles in English.
KiS Model is initialized with a GPT2-medium
model. We used the Adam optimizer, with a learn-
ing rate of 106, a batch-size of 1, using k-SCST
withk= 8.
Finetune Baseline is initialized with a GPT2-
medium model. We train using using standard
teacher forcing on the 40,000 samples in the paired
Newsela dataset , reserving 2,000 samples for val-
idation. We use the Adam optimizer, and use the
5https://github.com/huggingface/transformers
6https://github.com/nvidia/apexvalidation set to choose a learning rate of 105,
and a batch-size of 8, and run for 3 epochs before
seeing a plateau in the validation loss.
Discriminator Model is initialized with a
Roberta-base , and retrained every time the train-
ing buffer reaches 2,000 samples. The discrim-
inator is reset to the original Roberta-base each
time the training buffer is full. We use a standard
cross-entropy loss, the ADAM optimizer with a
learning rate of 105and a batch size of 8. Each
time we retrain, we run for 5 epochs, and check-
point one model after each epoch. The checkpoint
that achieves the highest performance on a valida-
tion set becomes the new discriminator for the next
round.
A.2 Human Evaluation Instructions
Figure A1 shows the instructions given to crowd-
worker participants for the manual evaluation.
•The entire HIT should take no more than 15
minutes:
(1) You will answer a pre-questionnaire.
(2) Read 4 short news stories and answer
comprehension questions about each.
•If you believe the answer is not in the
document, you can select the option “Answer
not in document”.
•There is no time limit for each individual
document or question.
•You can leave at any point but will not
complete the HIT.
• You can complete this task at most once.
•If you have a question/problem, contact us at
email .
Figure A1: Instructions given to participants of the
comprehension evaluation. Participants were recruited
on Amazon Mechanical Turk (MTurk), on which jobs
are named “HIT”.
A.3 Full Example of Generated Texts
Figure A2 is a complement to Figure 6, with the
five stimuli that were shown for the Covid Libraries
document.
A.4 Detailed of Human Evaluation Results
Table A1 details the timing and number of par-
ticipants for each combination of document and
stimuli.ORIGINAL [Lexile Grade 1 1] Each summer , libraries in St. Louis, Missouri, host many  types of free camps — yoga, chess and even a
Harry Pot ter “Sorting Hat Camp.” In 2020, camp dreams seemed far- fetched given the global coronavirus pandemic. That didn’t stop
St. Louis libraries, though.
Instead of canceling, they brought camp into kids’ homes. So children who signed up for ukulele camp got a beginner ’s guidebook,
instructional DVD and an actual ukulele in the mail. It was all free. In addition, camp sessions still occurred. Advisers met with kids
using virtual formats.
Joe Monahan, manager of youth services for the St. Louis  library system, says that of the 70 camps originally scheduled, 54 were held
virtually .
Paula Langsam, a youth services manager at the soon-to- reopen Martin Luther King Junior Memorial Library in W ashington, D.C.,
says, “In a way , our work has changed a lot. W e didn’t used to do videos a lot.”
Who manages the St Louis library kids programs?
Joe Monahan , Paula Langsam, St. Louis Camp Leaders
Were any camps in St. Louis cancelled?
Yes, NoHow many camps were scheduled, how many were run?
54 and 70, 70 and 54 , 70 and 0, 54 and 0
How did the Ukulele camp meet?
In the park, Virtually , Did not meetWhat camps did the libraries host?
Yoga, Chess , Pottery , UkuleleKIS MODEL [Lexile Grade 9] In the summer months, St. Louis has many free classes for  kids, including yoga, chess and a Harry
Potter “Sorting Hat Camp.” In 2020, camp dreams again seemed far-fetched  given the crisis. That didn’t stop St. Louis libraries, though.
They brought camp in. So kids who signed up for ukulele camp got a beginner ’s guidebook, a lesson DVD and a real ukulele in the
mailbox. It was all free. In addition, camp  sessions continued. Advisers tried out a virtual format.
Joe Monahan, the manager of youth services for the St. Louis library system, says that of the 70 camps originally scheduled, 54 were
held mostly .
Paula Langsam, a youth services manager at the Martin Luther King Junior library , says, “In a way , our work changed a lot. W e didn’t
do videos a lot.”NEWSELA [Lexile Grade 7] In St. Louis, Missouri, libraries hold many types of free camps in the summer . They have yoga, chess and
even a Harry Potter camp. In 2020, camp ideas seemed unlikely due to the spread of COVID-19. That did not stop St. Louis libraries,
though. They did not cancel their programs. They brought camp into kids' homes.
Children who signed up for ukulele camp got a beginner's book, instructional video and a ukulele in the mail. It was all free. In addition,
camp sessions still went on. Camp leaders met with kids over video calls.
Joe Monahan runs the kids programs for the St. Louis libraries. He said 70 camps were scheduled at first. Some were dropped but 54
were still held online.
Paula Langsam works at the Martin Luther King Junior Memorial Library in W ashington, D.C. It reopened on September 24. She said,
"Our work has changed a lot. W e didn't used to do videos a lot."
FINETUNE BASELINE [Lexile Grade 9] Each summer , St. Louis libraries host many types of free camps — yoga, chess and even a
Harry Potter "Sorting Hat Camp." In 2020, camp dreams seemed far-fetched given the global cough . However , the future looked
bright for libraries in St. Louis.
Instead of canceling, they brought camp into kids' homes. This is called ukulele camp. Kids signed up for it in the mail. They got a
beginner's guidebook, video and an actual ukulele to play . It was all free. Advisers met with kids using virtual formats.
Joe Monahan, manager of youth services for the St. Louis library system, says that 70 camps were originally planned. Of those, 54
were held mostly .
Paula Langsam is a youth services manager who works at the Martin Luther King Junior Memorial Library in W ashington, D.C. She
says, "In a way , our work has changed a lot. W e didn't used to do videos a lot."
ACCESS [Lexile Grade 1 1] Each summer , libraries in St. Louis, Missouri, has many dif ferent types of free camps that are yoga, chess
and even a Harry Potter gang Sorting Hat Camp. In 2020, camp dreams seemed far-fetched that there was the global coronavirus
pandemic. That did not stop St. Louis libraries, though.
Instead of being canceled, they brought camp into children's homes. So children who signed up for ukulele camp got a guidebook.
They also had an actual ukulelele in the mail. It was all free. In addition, camp meetings still happened. Advisers met with new children
using virtual formats.
Joe Monahan, also known as Joe Monahan, has youth services for the St. Louis library system says that of the 70 camps first started,
54 were held.
Paula Langsam, also known as Paula Langsam, is a youth services manager at the soon-to-reopen Martin Luther King Junior Library in
Washington, D. W e did not use to do many videos a lot.Figure A2: Complement to Figure 6. Example Task for the Comprehension Study. Participants were assigned
to one of five settings: original, Newsela, KiS, Finetune Baseline, and ACCESS. Participants were instructed to
answer the five comprehension questions.
Simplification Model
Document Id Original Newsela Sup. Base. ACCESS KiS
Marvel Show 152 (12) 209 (11) 140 (11) 209 (14) 126 (13)
Covid Libraries 167 (14) 180 (12) 182 (10) 190 (13) 171 (12)
Sustainable Food 163 (13) 144 (10) 181 (13) 242 (13) 154 (12)
Iceberg Collision 208 (14) 116 (11) 139 (12) 104 (12) 119 (12)
Version Aggregate 174 (53) 163 (44) 161 (46) 188 (52) 143 (49)
Table A1: Average time taken and number of participants in each of the document/stimuli combinations.
Also shown are aggregates (mean time taken and total number of participants).Model SARI BLEU %FKGL %Lexile Comp. Cov.
KiS Full 0.709 0.526 100 72 0.85 0.636
KiS No Fluency 0.718 0.611 99 95 1.02 0.901
KiS No Salience 0.695 0.591 100 65 1.01 0.701
KiS No Simplicity 0.672 0.617 51 23 0.92 0.809
Table A2: Automatic results of the three ablation models. SARI andBLEU are reference-based metrics. %
FKGL and% Lexile are the percentage of simplified paragraphs with a lower FKGL and Lexile score than the
original paragraph. Comp. is the average compression ratio (# of words), and Cov. is the average coverage score
of the simplifications.
A.5 Detail of Ablation Study Results
Table A2 details the metric results of the three ab-
lated models, an extension to Table 1. An example
output of each ablated model, illustrating the limi-
tation when a score component is missing, is given
in Figure 1.
One surprising element is that the model trained
without fluency achieves higher scores on almost
all metrics, compared to the full model. This sur-
prising fact is due to the fact that without fluency,
the model does not learn to generate full sentences
(see the example in Figure 1). Instead, the model
learns to concatenate high-scoring phrases together,
which can boost automatic metrics artificially. In
fact, the strong performance of a model generating
incomplete sentences reveals a limitation of current
automatic metrics, such as BLEU and SARI.