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Automatic detection of prerequisite relations between concepts in education has always been a challenging AI task for researchers. Identification of prerequisite relations enables students to study new subjects more effectively and systematically, while allowing instructors to better tailor their learning materials to students' needs. However, to accurately detect these relations, the AI system must understand the context and meaning behind each concept and how it relates to other concepts in the domain. This requires a deep understanding of the educational curriculum and the ability to analyze large amounts of text and data. Large language models (LLMs) are a recent innovation in AI. LLMs have the capability to understand and generate human-like text since they are trained on a vast amount of text from the internet, books, articles, and more. LLMs can also be fine-tuned to specialize in tasks such as document summarization, question answering, or detecting user sentiment in reviews. Fine-tuning is done with a smaller, task-specific dataset. In this work, we introduce strategies for fine-tuning LLMs to improve their capability to detect prerequisite relations between educational concepts. To the best of our knowledge, this is the first work that utilizes fine-tuned LLMs for prerequisite detection. Our evaluation results demonstrate that fine-tuned LLMs are effective models for prerequisite detection. Our fine-tuning process also facilitates the generation of explanations to shed light on the reasoning behind prerequisite relations. Datasets we generated and used in fine-tuning are made public for the research community. We hope our contributions can aid in organizing and presenting knowledge in education and serve as a foundation for future research in the field. [ABSTRACT FROM AUTHOR]

Nowadays, due to the constantly growing amount of textual information, automatic text summarization constitutes an important research area in natural language processing. In this work, we present a novel framework that combines semantic graph representations along with deep learning predictions to generate abstractive summaries of single documents, in an effort to utilize a semantic representation of the unstructured textual content in a machine-readable, structured, and concise manner. The overall framework is based on a well defined methodology for performing semantic graph parsing, graph construction, graph transformations for machine learning models and deep learning predictions. The employed semantic graph representation focuses on using the model of abstract meaning representation. Several combinations of graph construction and graph transformation methods are investigated to specify the most efficient of them for the machine learning models. Additionally, a range of deep learning architectures is examined, including a sequence-to-sequence attentive network, reinforcement learning, transformer-based architectures, and pre-trained neural language models. In this direction, a semantic graph representation of an original text is extracted, and then the present framework formulates the problem as a graph-to-summary learning problem to predict a summary of an original text. To the best of our knowledge, this formulation of graph-to-summary predictions in abstractive text summarization, without other intermediate steps in the machine learning phase, has not been presented in the relevant literature. Another important contribution is an introduction of a measure for assessing the factual consistency of the generated summaries in an effort to provide a qualitative evaluation. To assess the framework, an extensive experimental procedure is presented that uses popular datasets to evaluate key aspects of the proposed approach. The obtained results exhibit promising performance, validating the robustness of the proposed framework. [ABSTRACT FROM AUTHOR]

Fine-tuning a pre-trained sequence-to-sequence-based language model has significantly advanced the field of abstractive summarization. However, the early models of abstractive summarization were limited by the gap between training and inference, and they did not fully utilize the potential of the language model. Recent studies have introduced a two-stage framework that allows the second-stage model to re-rank the candidate summary generated by the first-stage model, to resolve these limitations. In this study, we point out that the supervision method performed in the existing re-ranking model of the two-stage abstractive summarization framework cannot learn detailed and complex information of the data. In addition, we present the problem of positional bias in the existing encoder–decoder-based re-ranking model. To address these two limitations, this study proposes a hierarchical supervision method that jointly performs summary and sentence-level supervision. For sentence-level supervision, we designed two sentence-level loss functions: intra- and inter-intra-sentence ranking losses. Compared to the existing abstractive summarization model, the proposed method exhibited a performance improvement for both the CNN/DM and XSum datasets. The proposed model outperformed the baseline model under a few-shot setting. [ABSTRACT FROM AUTHOR]

The transformer model is a famous natural language processing model proposed by Google in 2017. Now, with the extensive development of deep learning, many natural language processing tasks can be solved by deep learning methods. After the BERT model was proposed, many pre-trained models such as the XLNet model, the RoBERTa model, and the ALBERT model were also proposed in the research community. These models perform very well in various natural language processing tasks. In this paper, we describe and compare these well-known models. In addition, we also apply several types of existing and well-known models which are the BERT model, the XLNet model, the RoBERTa model, the GPT2 model, and the ALBERT model to different existing and well-known natural language processing tasks, and analyze each model based on their performance. There are a few papers that comprehensively compare various transformer models. In our paper, we use six types of well-known tasks, such as sentiment analysis, question answering, text generation, text summarization, name entity recognition, and topic modeling tasks to compare the performance of various transformer models. In addition, using the existing models, we also propose ensemble learning models for the different natural language processing tasks. The results show that our ensemble learning models perform better than a single classifier on specific tasks. [ABSTRACT FROM AUTHOR]

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