How to Improve Text Summarization and Classification by Mutual Cooperation on an Integrated Framework

Supervised machine learning studies are gaining more significant recently because of the availability of the increasing number of the electronic documents from different resources. Text classification can be defined that the task was automatically categorized a group documents into one or more predefined classes according to their subjects. Thereby, the major objective of text classification is to enable users for extracting information from textual resource and deals with process such as retrieval, classification, and machine learning techniques together in order to classify different pattern. In text classification technique, term weighting methods design suitable weights to the specific terms to enhance the text classification performance. This paper surveys of text classification, process of different term weighing methods and comparison between different classification techniques.

A massive of text that is generated every minute is increasing dramatically. Therefore, it is more and more important to find an effective model to automatic classify the amount of text. Topic models is the most powerful techniques in text classification. There are many research results in the field of topic model have been published in scholarly journals. The Latent Dirichlet Allocation (LDA) is one of the most popular topic models in text classification. Researchers have proposed many topic evolution models based on LDA to solve some specific problems in applications of text classification. And some joint models which based on topic models combined other algorithms have been studied to enhance the performance of text classification. In this paper, we investigated three categories topic models for text classification and briefly introduced their advantages and disadvantages in the applications of text mining. Also, we introduce the generated process of documents and illustrate the graphical model for each topic models.

Metaheuristic algorithms, inspired by natural phenomena and human-based strategies, offer versatile approaches to navigate diverse search spaces and adapt to dynamic environments. These algorithms, including evolutionary algorithms, swarm intelligence, bio-inspired methods, human-based approaches, and plant-inspired techniques, have found applications across diverse domains such as engineering, finance, healthcare, logistics, and telecommunications. In the text classification domain, metaheuristic techniques have emerged as powerful tools to enhance the accuracy, efficiency, and robustness of classification systems. By optimizing feature subsets, fine-tuning model parameters, and addressing challenges such as feature selection, dimensionality reduction, class imbalance, and noisy data, metaheuristic algorithms provide flexible solutions that adapt to various text datasets and tasks. This review paper comprehensively explores recent advancements in metaheuristic applications in text classification across six categories. From evolutionary-based methods to swarm-based approaches, bio-inspired techniques to physics/chemistry-based strategies, human-based methods to plant-based algorithms, researchers have leveraged diverse metaheuristic techniques to push the boundaries of text classification. Through a systematic analysis of recent research studies, this review provides insights into the strengths, limitations, and future directions of metaheuristic optimization in the context of text classification.

Text classification is a procedure of categorizing a text into classes or predefined labels, allowing effective analysis and organizing the large volume of textual data. In the growth of online textual data like news, digital platforms, or social media, an effective model for text summarization and classification. However, traditional models of text classification had difficulty capturing deep semantic dependencies and contextual embeddings among texts or words, which led to misclassification and minimized classification accuracy. To overcome these challenges, the research proposed Token Level Class Importance (TLCI) and Layer Wise Learning Rate Decay (LWLRD) named as TLLRD is integrated with Bidirectional Encoder Representations from Transformers (BERT) named as (TLLRD-BERT), which is utilized for multiclass violence text classification and enhanced classification performance. The model integrates LLRD to stabilize fine-tuning across transformer layers and TLCI to emphasize semantically significant words associated with aggressive or violent expressions. The proposed TLLRD-BERT attains an accuracy of 98.24%, 96.54% and 99.71% through YouTube comments dataset, the news headline dataset and the SMS spam detection dataset, respectively.

The Kampus Merdeka program, launched by the Indonesian Ministry of Education, Culture, Research, and Technology in 2020, aims to enhance students' skills through hands-on work experience. Considering the rising significance of social media, particularly Twitter, in gauging public opinion, this research focuses on analyzing the sentiment towards the Kampus Merdeka program. The primary objective is to classify the sentiments expressed in tweets related to the program and compare two feature extraction techniques—TF-IDF and FastText—to identify the best approach for transforming text data into numerical vectors. The sentiment classification model was built using the Support Vector Machine (SVM) algorithm, a machine learning technique known for its accuracy in text classification. A total of 16,730 tweets were collected and analyzed, yielding an accuracy of 73% for FastText and 72% for TF-IDF. Results show that FastText is more effective in capturing semantic relationships, leading to higher accuracy in sentiment classification. Findings indicate that the public sentiment towards the Kampus Merdeka program is predominantly positive (60.7%), with negative and neutral sentiments at 33.5% and 5.8%, respectively. The success of the FastText method underscores the importance of advanced feature extraction techniques in text classification. The novelty of this research lies in its use of FastText for educational policy evaluation, providing a new perspective on using sentiment analysis to assess public perception of educational programs.

BackgroundLarge language model (LLM) fine-tuning is the process of adjusting out-of-the-box model weights using a dataset of interest. Fine-tuning can be a powerful technique to improve model performance in fields like medicine, where LLMs may have poor out-of-the-box performance. The 2 common fine-tuning techniques are supervised fine-tuning (SFT) and direct preference optimization (DPO); however, little guidance is available for when to apply either method within clinical medicine or health care operations.ObjectiveThis study aims to investigate the benefits of fine-tuning with SFT and DPO across a range of core natural language tasks in medicine to better inform clinical informaticists when either technique should be deployed.MethodsWe use Llama3 8B (Meta) and Mistral 7B v2 (Mistral AI) to compare the performance of SFT alone and DPO across 4 common natural language tasks in medicine. The tasks we evaluate include text classification, clinical reasoning, text summarization, and clinical triage.ResultsOur results found clinical reasoning accuracy increased from 7% to 22% with base Llama3 and Mistral2, respectively, to 28% and 33% with SFT, and then 36% and 40% with DPO (P=.003 and P=.004, respectively). Summarization quality, graded on a 5-point Likert scale, was 4.11 with base Llama3 and 3.93 with base Mistral2. Performance increased to 4.21 and 3.98 with SFT and then 4.34 and 4.08 with DPO (P<.001). F1-scores for provider triage were 0.55 for Llama3 and 0.49 for Mistral2, which increased to 0.58 and 0.52 with SFT and 0.74 and 0.66 with DPO (P<.001). F1-scores for urgency triage were 0.81 for Llama3 and 0.88 for Mistral2, which decreased with SFT to 0.79 and 0.87, and then experienced mixed results with DPO, achieving 0.91 and 0.85, respectively (P<.001 and P>.99, respectively). Finally, F1-scores for text classification were 0.63 for Llama3 and 0.73 for Mistral2, which increased to 0.98 and 0.97 with SFT, and then essentially did not change with DPO to 0.95 and 0.97, respectively (P=.55 and P>.99, respectively). DPO fine-tuning required approximately 2 to 3 times more compute resources than SFT alone.ConclusionsSFT alone is sufficient for simple tasks such as rule-based text classification, while DPO after SFT improves performance on the more complex tasks of triage, clinical reasoning, and summarization. We postulate that SFT alone is sufficient for simple tasks because SFT strengthens simple word-association reasoning, whereas DPO enables deeper comprehension because it is trained with both positive and negative examples, enabling the model to recognize more complex patterns. Ultimately, our results help inform clinical informaticists when to deploy either fine-tuning method and encourage commercial LLM providers to offer DPO fine-tuning for commonly used proprietary LLMs in medicine.

Big multimedia data is heterogeneous in essence, that is, the data may be a mixture of video, audio, text, and images. This is due to the prevalence of novel applications in recent years, such as social media, video sharing, and location based services (LBS), etc. In many multimedia applications, for example, video/image tagging and multimedia recommendation, text classification techniques have been used extensively to facilitate multimedia data processing. In this paper, we give a comprehensive review on feature selection techniques for text classification. We begin by introducing some popular representation schemes for documents, and similarity measures used in text classification. Then, we review the most popular text classifiers, including Nearest Neighbor (NN) method, Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), and Neural Networks. Next, we survey four feature selection models, namely the filter, wrapper, embedded and hybrid, discussing pros and cons of the state-of-the-art feature selection approaches. Finally, we conclude the paper and give a brief introduction to some interesting feature selection work that does not belong to the four models.

Prediction of cause of death from forensic autopsy reports using text classification techniques: A comparative study

Text classification is one of the important applications of Natural Language Processing (NLP) in which text is classified into pre-defined categories or classes. Several state-of-the-arts have already been conducted on text classification in different Indian languages, however, limited researches on text classification performed in Odia. Moreover, the news headlines classification is a text classification technique which is performed in our study mainly focusing on Odia language. The Convolutional Neural Network (CNN) based Deep Learning technique is used to classify the Odia news headlines dataset available at Kaggle repository. The dataset consisting of approx. 15,200 Odia news headlines is split into training and testing sets of 80:20 ratio and is categorized into different classes, such as sports, business, and entertainment by using CNN. The efficacy and efficiency of the proposed CNN-based Odia news headlines classification is evaluated in contrary to other existing machine learning and deep learning models, such as Neural Networks, LSTM (Long Short-Term Memory), and Bi-LSTM (Bidirectional Long Short-Term Memory) with respect to different performance metrics like precision, accuracy, recall, F1-score, training time, and AUC-ROC (Area Under the Receiver Operating Characteristic Curve). The simulation results revealed that the proposed CNN model outperformed with respect to other models.

Text classification is a process of classifying textual contents to a set of predefined classes and categories. As enormous numbers of documents and contextual contents are introduced every day on the Internet, it becomes essential to use text classification techniques for different purposes such as enhancing search retrieval and recommendation systems. A lot of work has been done to study different aspects of English text classification techniques. However, little attention has been devoted to study Arabic text classification due to the difficulty of processing Arabic language. Consequently, in this paper, we propose an enhanced Arabic topic-discovery architecture (EATA) that can use ontology to provide an effective Arabic topic classification mechanism. We have introduced a semantic enhancement model to improve Arabic text classification and the topic discovery technique by utilizing the rich semantic information in Arabic ontology. We rely in this study on the vector space model (term frequency-inverse document frequency (TF-IDF)) as well as the cosine similarity approach to classify new Arabic textual documents.

The data mining is a task of analyzing the data and their hidden patterns. Therefore the classification and clustering approaches are used for performing this task. The classification of the data needs to be performing training over the similar patterns and using the training knowledge the upcoming patterns are recognized. In the data mining the text analysis is one of the popular aspects of the data mining. In this presented work the text analysis and their classification techniques are studied. In addition of that a new text classification approach is introduced for analysing the social media text. The proposed technique helps for automatically analysis of the text patterns and obtaining their positive and negative orientation according to the learned patterns. The proposed technique utilizes the word probability, sentence formation probability and the weighted indexing of the data for recognizing the orientation of the text data. The proposed technique organized in such manner by which the different combinations of the words can be analysed using the graphical approach. Therefore the computed probability and weights are used for generating the directed weighted graphs. Using this graph the upcoming technique is classified and their orientation is obtained. The implementation of the proposed text classification data model is provided using JAVA technology and their performance is also evaluated. The performance of the system is reported in terms accuracy, error rate, memory usage and the time complexity. According to the different experimental analysis the proposed technique is found accurate and efficient for text classification. Additionally that can also be extendable for finding the classes more than two classes.

Since electronic files were first introduced, automated text categorization has been a significant application and research area. Documents provide a wealth of important human information. Automated text categorization has become necessary due to the exponential increase in the number of documents that are readable by machines and available for either public or confidential access. The sheer volume of written material we handle daily makes text categorization necessary in today's world. Text categorization generally comprises two types of classification: genre-based and topic-based. Topic-based text categorization organizes texts based on their subjects, and the majority of information for genre classification comes from the internet, discussion boards, forums, and televised or print media. This document presents a comprehensive summary of the outcomes of several feature choices, classification techniques, and evaluation techniques. It offers a comprehensive overview and analysis of the research conducted on text categorization using several text classification methods.

Text classification plays an essential role in the medical domain by organizing and categorizing vast amounts of textual data through machine learning (ML) and deep learning (DL). The adoption of Artificial Intelligence (AI) technologies in healthcare has raised concerns about the interpretability of AI models, often perceived as "black boxes." Explainable AI (XAI) techniques aim to mitigate this issue by elucidating AI model decision-making process. In this paper, we present a scoping review exploring the application of different XAI techniques in medical text classification, identifying two main types: model-specific and model-agnostic methods. Despite some positive feedback from developers, formal evaluations with medical end users of these techniques remain limited. The review highlights the necessity for further research in XAI to enhance trust and transparency in AI-driven decision-making processes in healthcare.

Text classification is a supervised learning task for assigning text document to one or more predefined classes/topics. These topics are determined by a set of training documents. In order to construct a classification model, a machine learning algorithm was used. Training data is often a set of full-text documents. The training model is used to predict a class for new coming document. In this paper, we propose a text classification approach based on automatic text summarization. The proposed approach is tested with 2000 Vietnamese text documents downloaded from vnexpress.net and vietnamnet.vn. The experimental results confirm the feasibility of proposed model.

Chinese word segmentation is a basic research issue on Chinese information processing tasks such as information extraction; information retrieval; machine translation; text classification; automatic text summarization; speech recognition, natural language understanding and So on. And the ambiguity problem is one of the most difficult problems in Chinese word segmentation .The paper introduced several normal ambiguity discovery algorithm, and proposed a new method of ambiguity discovery that can discover many crossing ambiguity fields and covering ambiguity fields at the same time.