The purpose of this thesis was the further development of a rule set used in an automatic text simplification system, and the exploration of whether it is possible to improve the performance of a rule based text simplification system by manual training. A first rule set was developed from a thor- ough literature review, and the rule refinement was performed by manually adapting the first rule set to a set of training texts. When there was no more change added to the set of rules, the training was considered to be completed, and the two sets were applied to a test set, for evaluation. This thesis evaluated the performance of a text simplification system as a clas- sification task, by the use of objective metrics: precision and recall. The comparison of the rule sets revealed a clear improvement of the system, since precision increased from 45% to 82%, and recall increased from 37% to 53%. Both recall and precision was improved after training for the ma- jority of the rules, with a few exceptions. All rule types resulted in a higher score on correctness for R2. Automatic text simplification systems target- ing real life readers need to account for qualitative aspects, which has not been considered in this thesis. Future evaluation should, in addition to quantitative metrics such as precision, recall, and complexity metrics, also account for the experience of the reader.

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Many texts we encounter in our everyday lives are lexically and syntactically very complex. This makes them difficult to understand for people with intellectual or reading impairments, and difficult for various natural language processing systems to process. This motivated the need for text simplification (TS) which transforms texts into their simpler variants. Given that this is still a relatively new research area, many challenges are still remaining. The focus of this thesis is on better understanding the current problems in automatic text simplification (ATS) and proposing new data-driven approaches to solving them. We propose methods for learning sentence splitting and deletion decisions, built upon parallel corpora of original and manually simplified Spanish texts, which outperform the existing similar systems. Our experiments in adaptation of those methods to different text genres and target populations report promising results, thus offering one possible solution for dealing with the scarcity of parallel corpora for text simplification aimed at specific target populations, which is currently one of the main issues in ATS. The results of our extensive analysis of the phrase-based statistical machine translation (PB-SMT) approach to ATS reject the widespread assumption that the success of that approach largely depends on the size of the training and development datasets. They indicate more influential factors for the success of the PB-SMT approach to ATS, and reveal some important differences between cross-lingual MT and the monolingual v MT used in ATS. Our event-based system for simplifying news stories in English (EventSimplify) overcomes some of the main problems in ATS. It does not require a large number of handcrafted simplification rules nor parallel data, and it performs significant content reduction. The automatic and human evaluations conducted show that it produces grammatical text and increases readability, preserving and simplifying relevant content and reducing irrelevant content. Finally, this thesis addresses another important issue in TS which is how to automatically evaluate the performance of TS systems given that access to the target users might be difficult. Our experiments indicate that existing readability metrics can successfully be used for this task when enriched with human evaluation of grammaticality and preservation of meaning.

Creating easy-to-read text is an issue that has traditionally been solved with manual work. But with advancing research in natural language processing, automatic systems for text simplification are being developed. These systems often need training data that is parallel aligned. For several years, simple Wikipedia has been the main source for this data. In the current study, several readability measures has been tested on a popular simplification corpus. A selection of guidelines from simple Wikipedia has also been operationalized and tested. The results imply that the following of guidelines are not greater in simple Wikipedia than in standard Wikipedia. There are however differences in the readability measures. The syntactical structures of simple Wikipedia seems to be less complex than those of standard Wikipedia. A continuation of this study would be to examine other readability measures and evaluate the guidelines not covered within the current work.

Complex Word Identification (CWI) is a task of identifying complex words in text data and it is often viewed as a subtask of Automatic Text Simplification (ATS) where the main task is making a complex text simpler. The ways in which a text should be simplified depend on the target readers such as second language learners or people with reading disabilities. In this thesis, we focus on Complex Word Identification for Swedish. First, in addition to exploring existing resources, we collect a new dataset for Swedish CWI. We continue by building several classifiers of Swedish simple and complex words. We then use the findings to analyze the characteristics of lexical complexity in Swedish and English. Our method for collecting training data based on second language learning material has shown positive evaluation scores and resulted in a new dataset for Swedish CWI. Additionally, the built complex word classifiers have an accuracy at least as good as similar systems for English. Finally, the analysis of the selected features confirms the findings of previous studies and reveals some interesting characteristics of lexical complexity.

This thesis presents the development and evaluation of context-aware Lexical Simplification (LS) systems for the Swedish language. In total three versions of LS models, LäsBERT, LäsBERT-baseline, and LäsGPT, were created and evaluated on a newly constructed Swedish LS evaluation dataset. The LS systems demonstrated promising potential in aiding audiences with reading difficulties by providing context-aware word replacements. While there were areas for improvement, particularly in complex word identification, the systems showed agreement with human annotators on word replacements. The effects of fine-tuning a BERT model for substitution generation on easy-to-read texts were explored, indicating no significant difference in the number of replacements between fine-tuned and non-fine-tuned versions. Both versions performed similarly in terms of synonymous and simplifying replacements, although the fine-tuned version exhibited slightly reduced performance compared to the baseline model. An important contribution of this thesis is the creation of an evaluation dataset for Lexical Simplification in Swedish. The dataset was automatically collected and manually annotated. Evaluators assessed the quality, coverage, and complexity of the dataset. Results showed that the dataset had high quality and a perceived good coverage. Although the complexity of the complex words was perceived to be low, the dataset provides a valuable resource for evaluating LS systems and advancing research in Swedish Lexical Simplification. Finally, a more transparent and reader-empowering approach to Lexical Simplification isproposed. This new approach embraces the challenges with contextual synonymy and reduces the number of failure points in the conventional LS pipeline, increasing the chancesof developing a fully meaning-preserving LS system. Links to different parts of the project can be found here: The Lexical Simplification dataset: https://github.com/emilgraichen/SwedishLSdataset The lexical simplification algorithm: https://github.com/emilgraichen/SwedishLexicalSimplifier

The ability to read and comprehend text is essential in everyday life. Some people, including individuals with dyslexia and cognitive disabilities, may experience difficulties with this. Thus, it is important to make textual information accessible to diverse target audiences. Automatic Text Simplification (ATS) techniques aim to reduce the linguistic complexity in texts to facilitate readability and comprehension. However, existing ATS systems often lack customization to specific user needs, and simplification data for languages other than English is limited. This thesis addressed ATS in a Swedish context, building upon novel methods that provide more control over the simplification generation process, enabling user customization. A dataset of Swedish paraphrases was mined from a large amount of text data. ATS models were then trained on this dataset utilizing prefix-tuning with control prefixes. Two sets of text attributes and their effects on performance were explored for controlling the generation. The first had been used in previous research, and the second was extracted in a data-driven way from existing text complexity measures. The trained ATS models for Swedish and additional models for English were evaluated and compared using SARI and BLEU metrics. The results for the English models were consistent with results from previous research using controllable generation mechanisms, although slightly lower. The Swedish models provided significant improvements over the baseline, in the form of a fine-tuned BART model, and compared to previous Swedish ATS results. These results highlight the efficiency of using paraphrase data paired with controllable generation mechanisms for simplification. Furthermore, the different sets of attributes provided very similar results, pointing to the fact that both these sets of attributes manage to capture aspects of simplification. The process of mining paraphrases, selecting control attributes and other methodological implications are discussed, leading to suggestions for future research.