1. Introduction

Comprehension of complex sentences creates an immense hurdle for new language learners, particularly those who lack skill in the language, struggle with reading, and people with cognitive disabilities [1–5]. Having simplified text can greatly facilitate such individuals, where sentence simplification is a strategy that breaks down difficult phrases into their simplest form to improve readability [6–8]. The importance of simplicity was emphasized in the late 1990s by initiatives that stressed conveying essential information in easily understandable manner and proposed suggestions to enhance the clarity of communication [9,10].

It has been demonstrated that easily comprehensible writing provides advantages to readers, especially individuals with lower levels of literacy [11], language learners [12,13]. It also aids individuals diagnosed with dyslexia [14], autism [15], aphasia [16]. Individuals with learning difficulties tend to perceive reduced sentences as more comprehensible [17,18]. Text simplification also has wide applications in Natural Language Processing(NLP) tasks as a prepossessing step, for example,to improve the performance of parsers [19], summarizers [20], and semantic role labelers [21].

Most of the studies on simplification have concentrated on English where source texts and related simplified texts are available and can be automatically aligned such as English Wikipedia [22]. This remains and unexplored area for most languages due to unavailability of simplification corpora.

To facilitate the study of complexity and simplicity parameters across various languages, it is essential to possess a corpus comprising pairs of sentences along with their simplified versions. Such corpora, known as sentence-aligned simplification parallel corpora, have been compiled for multiple languages. For instance, in English, resources like the Simple Wikipedia corpus PKWP [23], Newsela [24], Onestop [25], and ASSET [26] have been established. Similar efforts have created resources for Spanish (An-Cora [27]), Italian (ERNESTA [18]), French (CLEAR [28] and Alector [29]), and other languages. The development of such simplification corpora, containing a sufficient number of complex sentences and their corresponding simplified versions, is a critical first step. Unfortunately, there is currently a lack of study on simplifying sentences in low-resource languages.

Punjabi is an Indo-Aryan language that is widely spoken in Pakistan and India (https://simple.wikipedia.org/wiki/Punjabi_language). The primary focus of our research is Punjabi language written using the Shahmukhi (شاہ مُکھی) script. Despite the huge number of speakers (https://en.wikipedia.org/wiki/Punjabi_language), this particular variation has received relatively less attention, particularly within the fields of natural language processing and sentence simplification. Punjabi is one of the world’s top ten languages and is written using “Gurmukhi” and “Shahmukhi” scripts based on geographical location and spoken in a variety of dialects (https://en.wikipedia.org/wiki/Punjabi_dialects_and_languages) (Accessed on: 15 september 2025). The language’s large user base gives it considerable weight in the field of natural language processing.pujabi text book syllabus is available at (https://pctb.punjab.gov.pk/) (Accessed on: 1-december-2025) which also shows the importance of this language. To the best of our knowledge, the only prior work on Punjabi text simplification is by [30], which focuses on the Gurmukhi script. While their work is highly relevant as it addresses the same language, the significant orthographic, morphological, and lexical differences between the Gurmukhi and Shahmukhi scripts present unique challenges that necessitate a separate investigation. This work is, therefore, the first to address simplification for the Shahmukhi script.

The foundation of our work is a manually crafted parallel corpus. It consists of 1,400 original complex sentences, each manually simplified to create a high-quality reference simplification, which we designate as S1. This gives us 1,400 {Complex, S1} core pairs.

To augment this data and introduce syntactic diversity, we generated three additional simplified variants for each complex sentence. Using the manually created S1 simplifications as a base, we applied a set of rules for three primary operations: lexical substitution, deletion, and insertion. This process produced three new versions for each sentence: S2, S3, and S4.

Therefore, for every one of the 1,400 complex sentences, we have a quadruplet of four simplified versions ({S1, S2, S3, S4}), resulting in a total of 1,400 × 4 = 5,600 simplified sentences. The entire corpus comprises 1,400 complex sentences aligned with 5,600 simplified sentences, organized into 1,400 quadruplets. An example quadruplet is shown in Table 4.

The contribution can be summarized as follows:

• Creation of a corpus named PUSIM for the evaluation of Punjabi sentence simplification model,will be followed by its public release at https://github.com/sabdul111/Punjabi-Simplification-Corpusit will be the first open source simplification dataset in Punjabi language.
• Corpus contains manual simplification with three different versions of per original sentence.

This paper is organized as follows: Section presents a brief overview of sentence simplification. Section 2 describes our proposed approach and system architecture and begins with the process of corpus creation, followed by the main steps involved in building the PUSIM corpus. Two types of evaluations are then conducted: automatic evaluation, discussed in Section 5, and human evaluation, presented in Section 3. A readability analysis is also performed to further assess the results.

Section 1 reviews the related work, and finally, Section 7 concludes the paper with a general discussion and future directions.

2. Related work

Various methods have been used to simplify sentences in the past, including rules that were hand-designed [12,13,31–33] using synonyms and paraphrases for syntactic and lexical simplification [34–36], husing embeddings [37] and viewing simplification as a monolingual machine translation problem where operations are learned from examples of complex-simple sentence pairs [23,38–40]. Many unsupervised techniques have been suggested in recent research to train simplification models without a labeled simplification corpus, which is a solution to the problem of low-resource languages and the shortage of parallel SS corpus [41–44]. MUSS [45] achieves impressive results in Spanish and French, surpassing even the supervised state-of-the-art. Many subsequent studies have employed this approach in their attempts to simplify English sentences. These studies have utilised various methods such as phrase-based machine translation [46] and syntax-based machine translation [23,31] or neural machine translation [22,47]. According to a comprehensive manual evaluation, the neural machine translation (MT) model developed by [47] demonstrates superior performance compared to the phrase-based MT model proposed by [40], as well as the syntax-based MT model introduced by [23,31,47].

3. Corpus creation

The study utilized monolingual resources from the EMILLE Corpus (Baker et al., 2008), distributed by the European Language Resource Association (ELRA). The Punjabi component of this corpus, which comprises traditional narratives and stories, was selected for this work. The raw corpus contains 375,366 sentences.

A subset of 1,400 sentences was curated from this larger corpus for the purposes of this study. The sentence selection procedure was designed to align the data with the target audience—individuals with a basic level of understanding and Grade 12 to Grade 16 students. The exclusion criteria were as follows:

Temporal Relevance: Sentences containing vocabulary, idioms, or cultural references that are largely archaic and no longer in common use in modern, everyday Punjabi were excluded. This ensures the study’s relevance to contemporary language learners.

Lexical Modernity: Preference was given to sentences where words appear in their current, evolved morphological forms, rather than their historical or literary variants.

This process resulted in a manageable and relevant dataset focused on high-frequency, modern Punjabi usage, upon which the subsequent simplification tasks were performed.

Motivation for Choosing the EMILLE Corpus:A primary motivation for selecting the EMILLE Corpus was its status as a foundational, broad-coverage resource for languages, providing a robust and diverse starting point for linguistic research. While the corpus contains historical and literary material, its size and variety allowed for the intentional selection of a modern-facing subset. This approach of curating a relevant sub-corpus from a larger, more general resource is a established practice in NLP, allowing us to leverage the corpus’s strengths (scale, authenticity) while mitigating its limitations (temporal spread) for our specific task of text simplification.

3.1. Simplification scheme

In order to enhance the ease of understanding without altering the essential information, manual simplification is used to ensure correctness. The simplification methodology is consistent with previous works [26,43,59,60] and [28,50]. As we have adopted the same core simplification operations that were formally established and applied in the referenced literature. This means we employed identical linguistic strategies—such as rephrasing, insertion, deletion, and reordering—following the definitions, guidelines, and practical implementations described in these prior studies. By applying these well-recognized operations to the Punjabi language, we ensured that our simplification process aligns with internationally accepted methodologies.

First version of the corpus was mainly based on rephrasing, insertion, deletion, substitution, merging and reordering. The comparison of these operations is presented in Table 1. The 3 subsequent versions were created using lexical and phrase substitution.

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Table 1. Simplification operations were applied in different corpora.

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Two annotators worked on simplification which took almost 500 man hours. Both were Punjabi natives with fluent proficiency with one being a linguist and Punjabi expert. The steps are detailed below with relevant examples, an overview of the procedure is depicted in Figurefig:simplification-operations. One annotator performed the primary simplification work on the corpus while the second annotator carried out a thorough, independent review. Each simplified sentence was checked repeatedly in an iterative manner to ensure accuracy and consistency: the reviewer inspected every sentence, suggested corrections where necessary, and the pair discussed and resolved any disagreements. This careful, repeat-check process served as an internal quality-assurance step to guarantee that the final simplified sentences are both faithful to the originals and easy to understand. They are not paid. The existing simplification corpora have been developed for several languages such as English, Italian, and Turkish and Chinese In contrast, PUSIM is designed for Punjabi, addressing the lack of simplification resources for low-resource languages.

3.2. Lexical and phrase substitution

Lexical substitution is a tool for simplifying sentences by replacing difficult words with easier equivalents without altering the intended meaning [6]. For instance تھاں in Punjabi means lace” which is replaced with its simpler alternative جگہ, which is easier to understand. In phrase level substitution a complex phrase is replaced with a correspondingly easier phrase for example " بڑی دھیان گوچری اے” which means “to focus” is replaced with " بڑی غور کرن والی اے” “focus”. The guidelines of these decisions were rooted in the principal of modern frequency usage in punjabi. This approach is consistent with the manual simplification paradigm where annotators apply linguistic expertise to judge complexity as shown in the guidelines 6 The context is properly followed so that the meaning of sentence remains unaltered. 89% of our sentences underwent as shown in Fig 1.

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Fig 1. Simplification operations of manual corpora.

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3.2.1. Complex:Simple Lexicon.

A by-product of lexical and phrase substitution process was a 630 entry lexicon containing 390 word level and 254 phrase level entries. Some examples are shown in Table 2 and 3.

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Table 2. Some examples from the complex:simple lexicon.

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Table 3. Frequency-based Lexicon Sample.

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few lexicon with the word level frequencies are mentioned.

3.7. Versions of simplification

Three additional versions of the simplification corpus were created using the simplified corpus as a base. These simplified versions were created by adding, removing, or changing words in the original simplification. Each sentence now has four different simplified versions. These are reffered to as Expanded Simplification Variants (ESV). Table 4 shows the four variants], S1 denotes first level simplification, and S2 til S3 denote the next simplifications.

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Table 4. Versions simplification: The first sentence S1 is the main simplified sentence, and the next three, S2, S3, and S4, are lexically simplified versions of it.

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We rephrase the word انتاں دا with بہت and in the next sentence, the main word شرارتی is changed with مستا so by reordering, rephrasing,and deletionwe have created different variants to choose the best possible version of simplification (Table 5).

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Table 5. S1 is the syntactic simplification, whereas S2 - S4 denote the lexical simplifications of S1.

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Fig 1 and Table 5 show the percentage of each simplification operation on the x-axes applied during the syntactic simplification S1. Rephrasing was the most significant operation with 89% of sentences being rephrased, this is in agreement with previous works, e.g. [46] used 65% of rephrasing operation for English simplification. Reordering was the second most significant operation with 45% sentences. Only 4 % of sentences employed splitting and merging. Punjabi tends to require more structural adjustments during simplification due to morphological complexity. Many original sentences contained cultural idioms and compounding that required rephrasing rather than simple lexical or deletion operations. Therefore, the higher rephrasing percentage reflects linguistic characteristics of Punjabi rather than inconsistency. The high rephrasing percentage also shows how much careful effort we put into manually simplifying the corpus. Punjabi sentences often cannot be simplified by just replacing a word—they need to be rewritten for clarity. This required our annotators to spend more time and attention on every sentence.

4. Human evaluation

Human evaluation was done by four graduate native Punjabi speakers on 30 randomly selected sentences. The sentences were evaluated for adequacy, fluency, and simplicity. The annotators were asked to rank the sentence pairs based on the three parameters shown in Table 6. Q1 measures simplicity, Q2 is based on the adequacy, which measures meaning preservation, and Q3 measures fluency of the sentences. Ranking was done using Likert scale [61], the participants were asked to rank the sentence in the range of 1–5. Possible rankings were: 1 for “strongly disagree,” 2 for “disagree”, 3 for “average,” 4 for “agree” and 5 is for “strongly agree.” The pairwise inter-annotator reliability for the 30 phrases evaluated by the four evaluators. However, our sample size is consistent with established practice in human evaluation for text simplification, where researchers commonly use 20–50 sentences for subjective quality assessments such as simplicity, fluency, and adequacy. Several prior works on text simplification related NLP tasks (e.g., readability evaluation, summarization quality judgments, and controlled generation) have used small but carefully selected random samples to assess human-perceived quality. These studies argue that human evaluation is costly, time-intensive, and requires expert annotators, therefore smaller but representative samples are widely accepted when:Annotators are expert-level (as in our case: four graduate native Punjabi speakers),

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Table 6. Guidelines for human evaluation.

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Sampling is random, ensuring no selection bias, and Evaluation focuses on subjective dimensions rather than model-level statistical performance. Fleiss kappa score, which is used commonly used to measure inter-annotator agreement for more than two annotators was calculated on the corpus.

4.1. Fleiss kappa: Inter-annotator agreement scores

Fleiss kappa [62] is a statistical measure used to assess the degree of agreement among multiple raters when assigning categorical ratings to items or classifying items. It is an extension of Cohen’s kappa [63], which is normally used to assess the agreement between two raters.

Generally, for Fleiss kappa scores greater than 0.75 indicate excellent agreement, values between 0.75 and 0.40 indicate good to fair agreement, and values less than 0.40 indicate poor agreement (https://www.datanovia.com/en/blog/kappa-coefficient-interpretation/). Table 7 shows our Fleiss’ kappa scores for different aspects, i.e., fluency, adequacy, and simplicity based on the super scale and lower scale. All aspects show excellent agreement on the super scale and a good agreement on the lower scale, on average it is excellent agreement on both scales 0.89, 0.75. These scores indicate that the PUSIM corpus demonstrates reasonable consistency among raters, especially considering the complexity of evaluating fluency, adequacy, and simplicity. Super Scale (Positive ratings): 4–5

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Table 7. Inter-annotator agreement scores using Fleiss Kappa, the standard deviation are given in brackets.

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Lower Scale (Non-positive ratings): 1–3

This binary separation follows standard practice in human evaluation studies where upper Likert levels represent high-quality outputs and lower/mixed ratings are grouped together for contrastive analysis.

5. Simplicity and readability

Readability metrics measure the degree, that how easy or hard is a text to understand [64]. These metrics use mathematical formulas based on the factors associated with the lexical content of the text. These factors include the number of characters, syllables, words, sentences, difficult words, average sentence length, average word length and average difficult words etc.

Flesch Reading Ease (FRE) [65] and Flesch-Kincaid Grade Level (FKGL) [66], SMOG [67] and Automated Readability Index (ARI) [68] were used to grade PUSIM simplifications. Since, no prior work exists on readability parameters for Punjabi, conventional readability parameters tweaked according to lexical features of Punjabi were chosen. Flesch Reading Ease scores range from 0 to 100. If the score is high, the text is easy to read. If it’s low, the text is harder to understand. Table 8 outlines the readability ranges for with each metric indicating average or skilled levels.

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Table 8. Score range for readability metrics.

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5.1. Readability metrics

The metrics we selected for analysis FKGL, ARI, and SMOG are directly related to complexity. A higher score indicates more complexity, while a lower score means the text is simpler. However, with FRE, it’s the opposite: a higher score means the text is simpler. The goal was to get a relative, comparative score. We used the exact same formula to calculate a score for both the original complex text and our new simplified text. Even with this simple method, our simplified corpus consistently showed a better (higher) readability score than the original complex text. In Table 9 we have some interesting observations. The lowest score on FKGL [66] indicates that even a difference of small points is important in categorizing the level of text.

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Table 9. Scores of original and Simplified sentences against FKGL, FRE, ARI, SMOG, LIX and Coleman-Liau.

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The ARI (Automated Readability Index) and LIX scores indicate notable improvements in the readability of the simplified corpus compared to the original text. The main formula of LIX score is depend on the number of difficult words. The method was developed in 1968 by Swedish linguist and is widely used in European countries to assess the readability of documents. We calculate the difficult word as the word with greater than or equal to 5 characters is considered difficult. After applying the formula,

(1)

Where, W = Number of words. S = Number of sentences. WD = Number of difficult words. The LIX score for the original corpus was 60, classifying it as “very difficult.” After simplification, the score reduced to 53, categorizing it in the “skilled” range. This shift reflects a reduction in the number of difficult words and improved overall readability. The ARI score for the original corpus was 7.0, which places it in the “average” complexity range. After simplification, the score improved to 5.3, placing it in the “basic” range.

While improvements were observed in all metrics, the scores of the simplified corpus remain within their respective readability ranges, validating the enhanced accessibility of the text. The FKGL score indicates the minimum reading level necessary to comprehend the content. But this metric can easily count the number of English words and syllables, and we apply the formula to our Punjabi corpus to get the results. Even though this metric is generally for English, The simplified corpus shows better results simplying that the readability is improved. FRE [65] indicates a higher score means the text is simpler. To calculate the FRE score, we need words, sentences, syllables, and characters. We made adjustments to the syllable counting algorithm. We defined a syllable as a unit of sound containing a vowel. Our algorithm scans each word and counts a syllable for every occurrence of the following core vowel characters: The syllable count in Punjabi Shahmukhi is [”ا” ,“آ” ,“و” ,“ؤ” ,“ی” ,“ے” ,“ہ”]. This adjustment improves the dependability of syllable-counting readability measures which shows the original corpus has 94.5 and in case of simplified corpus its 100. The SMOG score changed minimally from 3.0 (original) to 3.1 (simplified). While this indicates only a minor difference, the metric remains somewhat unreliable for Punjabi due to its reliance on polysyllabic word counts.

We made a number of adjustments to how we computed readability ratings for our Punjabi corpus so that we could better reflect the specifics of that language. After making these adjustments, we examined our simplified corpus for readability metrics and found that it was simplified.

The following lexical features are to be extracted from the given text for several readability formulas.

• Number of characters in the text.
• Number of syllables in a text.
• Number of words in the text.
• Number of sentences in the text.
• Number of difficult words. It is different for every language.
• Average sentence length. Calculated using the following formula.(2)
• Average word length. Calculated using the following formula.(3)
• Average number of difficult words. Calculated using the following formula.(4)

6. Automatic text simplification model

Phrase-based MT [69] has been a popular choice to develop Automatic Text Simplification (ATS) systems, especially when small amounts of simplification corpus are available [34,38,39]. Recent Neural text simplification [13,22,47,70,71] methods have significantly outperformed the SMT-based systems, but they require huge amount of corpora, which makes the SMT the most suitable choice for our systems. Multilingual sequence-to-sequence models like mBART, mT5, NLLB-2005, and mmT517 indeed support Shahmukhi and offer state-of-the-art performance for translation tasks. These models excel in zero-shot cross-lingual transfer (e.g., mT5 reduces source language hallucination from 7% to 9% in zero-shot settings) [72]. Massive multilingual coverage (e.g., NLLB-200 supports 200 languages, including 55 African languages) [73]. Denoising pre-training (e.g., mBART improves low-resource MT by up to 12 BLEU points) [74]. However, these models require large-scale corpora and substantial computational resources for training and fine-tuning. For example, NLLB-200 has 54 parameters and was trained on a supercluster [75]. Recent multilingual sequence-to-sequence models such as mBART, mT5, NLLB-2005, and mmT5 indeed support Shahmukhi and have achieved state-of-the-art performance in translation-related tasks. These models perform particularly well in zero-shot cross-lingual transfer; for example, mT5 reduces source-language hallucination from 7% to 9% in zero-shot settings [72]. They also provide massive multilingual coverage (e.g., NLLB-200 supports around 200 languages, including 55 African languages [73]), while denoising pre-training strategies such as those used in mBART have been shown to improve low-resource machine translation by up to 12 BLEU points [74]. However, such models typically require large-scale corpora and significant computational resources for both training and fine-tuning. For instance, NLLB-200 consists of approximately 54B parameters and was trained on a supercluster [75]. In comparison, Phrase-Based Statistical Machine Translation (PBSMT), particularly Moses, remains more suitable for low-resource Automated Text Simplification (ATS) tasks. These systems are more data-efficient and can perform reasonably well even with limited parallel data. In addition, data selection techniques can help optimize small datasets by extracting pseudo in-domain sentences. Furthermore, SMT-based ATS systems have demonstrated effectiveness for low-resource languages in prior studies [76], making them a practical and appropriate choice for Punjabi text simplification in resource-constrained scenarios.

We developed standard phrase-based Statistical Machine Translation (SMT) systems using the default configurations of the Moses toolkit [77]. For language modeling, we used a 5-gram KenLM model [78]. Language models for each system were trained on the target side of the parallel corpus. Word alignment was performed using Giza++ [79] with the widely adopted grow-diag-final-and-symmetrization technique. Maximum sentence length of 100 for word alignment and a distortion limit of 6 with a 100-best list for reordering were used. We used msd-bidirectional-fe lexical reordering model with a phrase table limit of 5.

Train, development and test sets were split in 80:10:10 ratio. SARI score was computed for 2 different test sets. The self test sethas only a single reference from the test split of the same system. The combined test set contains multiple references, where four reference simplifications are given for each sentence. SARI supports multiple references, enabling a better evaluation of simplification scores. Parameters in Moses were fine-tuned on development data with the Minimum Error Rate Training (MERT) tool.

7. Experiments and results

To measure the credibility of our corpus for automatic simplification, we built a cascade of phrase-based simplification systems using the original simplification S1 and expanded simplification variants (S2,S3,S4) (see section 2.6). Table 10 shows the corpus combinations and their corresponding BLUE and SARI scores.

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Table 10. Blue and Sari scores of all models, Syntactic Simplification and Expanded Simplification Variant based models are shown in italic.

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Table 10 details our experiments with different combinations of the simplification corpora. The first model M1 is built using the corpus and our initial syntactic simplification scheme and achieve the BLEU score of 30.3 and SARI score of 40.8. The concatenation of S1 and S3 shows 37.5 BLEU score and for the other concatenation of data results shown in. Our corpus may not be sufficient to build successful models but its useful to test generalization of model for simplification of sentences.

The results of the models shown in Table 10 indicate that the performance of the model improves as the size of the data increases. Specifically, the S1 + S2 + S3 + S4 model, which is the largest corpus, achieves the highest BLEU score of 59.6. Similarly, in the self-references, the S1 + S2 + S3 + S4 model also achieves the highest BLEU and SARI scores of 59.6 and 45.3, respectively. These findings suggest that the size of the corpus is a significant factor in the performance of the SMT-based simplification approach. However, in some cases, the use of multiple references can lead to a lower score for the model, as is the case with the SS + LS model trained on S1 + S4. This could be due to the fact that the model is generating simplified sentences that are different from the available reference sentences, but are still valid simplifications. In such cases, it is important to manually evaluate the output of the model to ensure that it is generating high-quality simplifications.

The results of SS + LS model demonstrate the potential of using SMT-based approaches for text simplification and provide a strong justification for the use of syntactic simplification techniques in this context. However, in some cases, the use of multiple references can lead to a lower score for the model, as is the case with the SS + LS model trained on S1 + S4. This could be due to the fact that the model is generating simplified sentences that are different from the available reference sentences but are still valid simplifications. In such cases, it is important to manually evaluate the output of the model to ensure that it is generating high-quality simplifications (Table 11).

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Table 11. Comparison of output of all systems M1- M6 shows sentences generated by models and reference sentence presents mannualy simplification.

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In the first sentence,Model M1 substituted the word “پاروں” with “پہنچ سکے,” M5 and M6 introduced lexical substitutions like “چھڈ کے” and “نا گھر چھڈ کے” with M6 closely aligning with the reference sentence by emphasizing the key aspect of “leaving home to work in films. In the second sentence, M1 replaced “پاروں” with “تو” to express the causal relationship in simpler terms, aligning with the original intent but modifying the structure. M4 made a minor lexical substitution by replacing “پاروں” with “تو” preserving meaning. M5 and M6 substitute phrases like “پيداہون دے کارن” and “رکھيا سی” M6 aligned most closely with the reference sentence by accurately conveying the meaning of being named due to being born in an old Punjabi village.

8. Conclusion

This paper is committed to facilitating research on Punjabi sentence simplification. PUSIM, a new dataset for the evaluation of Punjabi SS models. The simplifications in PUSIM were written manually by human annotators, and the simplification operations are also labeled. An in-depth analysis of PUSIM includes word insertion, deletion, substitution, rearrangement, and rephrasing, each applied independently to assess its individual impact on readability. Through careful evaluation using automated reading evaluations, it has been shown that this method works, and steps towards making the Punjabi Shahmukhi writing system less complicated also have been made. Our central hypothesis—that a larger and more diverse training corpus would lead to better simplification models—was partially proven. The results strongly confirm that corpus size is a dominant factor in achieving high fluency, as evidenced by the best-performing M6 model which leveraged the entire. However, our findings also reveal that the quality and nature of the data are equally critical. Additionally, this explanation shows how careful research methods were and highlights the clarity and precision with which this simplification means was implemented from the outset.

Future research directions include expanding PUSIM with more diverse sentence structures, developing more sophisticated simplification models, and investigating the impact of different simplification strategies on various linguistic factors. This study provides a foundational dataset and a benchmark for future work in Punjabi text simplification. It establishes that effective simplification requires a careful balance between data quantity and the consistent application of simplification rules. The insights gained are a crucial step toward developing automated tools to enhance readability and accessibility for Punjabi readers.

8. Limitations

• Simplification Scope: Our simplification strategies focused primarily on lexical and syntactic complexity. We did not explicitly address more complex semantic simplifications, such as breaking down long concepts, adding explanations, or altering discourse structures.
• Generalizability of Methods: The syntactic simplification rules and ESV strategies were developed for a specific dataset. Their effectiveness and general applicability to any arbitrary Punjabi text require further validation.
• Our findings are therefore a baseline, and future work would benefit from applying more modern techniques with larger datasets.