Automatic text summarisation is obtaining a subset that accurately represents the main text. A quality summary should contain the maximum amount of information while avoiding redundant information. Redundancy is a severe deficiency that causes unnecessary repetition of information within sentences and should not occur in summarisation studies. Although many optimisation-based text summarisation methods have been proposed in recent years, there exists a lack of research on the simultaneous optimisation of scope and redundancy. In this context, this study presents an approach in which maximum coverage and minimum redundancy, which form the two key features of a rich summary, are modelled as optimisation targets. In optimisation-based text summarisation studies, different conflicting objectives are generally weighted or formulated and transformed into single-objective problems. However, this transformation can directly affect the quality of the solution. In this study, the optimisation goals are met simultaneously without transformation or formulation. In addition, the multi-objective saplings growing-up algorithm (MO-SGuA) is implemented and modified for text summarisation. The presented approach, called Pareto optimal, achieves an optimal solution with simultaneous optimisation. Experimentation with the MO-SGuA method was tested using open-access (document understanding conference; DUC) data sets. Performance success of the MO-SGuA approach was calculated using the recall-oriented understudy for gisting evaluation (ROUGE) metrics and then compared with the competitive practices used in the literature. Testing achieved a 26.6% summarisation result for the ROUGE-2 metric and 65.96% for ROUGE-L, which represents an improvement of 11.17% and 20.54%, respectively. The experimental results showed that good-quality summaries were achieved using the proposed approach.

In biomedicine, the expansive scientific literature combined with the frequent use of abbreviations, acronyms, and symbols presents considerable challenges for text processing and summarization. The Unified Medical Language System (UMLS) has been a go-to for extracting concepts and determining correlations in these studies; hence, the BioGraphSum model introduced in this study aims to reduce this UMLS dependence. Through adoption of an innovative perspective, sentences within a piece of text are graphically conceptualized as nodes, enabling the concept of “Malatya centrality” to be leveraged. This approach focuses on pinpointing influential nodes on a graph and, by analogy, the most pertinent sentences within the text for summarization. In order to evaluate the performance of the BioGraphSum approach, a corpus was curated that consisted of 450 contemporary scientific research articles available on the PubMed database, aligned with proven research methodology. The BioGraphSum model was subjected to rigorous testing against this corpus in order to demonstrate its capabilities. Preliminary results, especially in the precision-based and f-score-based ROUGE-(1–2), ROUGE-L, and ROUGE-SU metrics reported significant improvements when compared to other existing models considered state-of-the-art in text summarization.

Artan internet ve sosyal medya kullanımı ile sosyal medya ve online haber siteleri bilgi içeriklerini oluşturmada ve yaymada önemli kaynaklar haline gelmişlerdir. Ancak online bilginin miktarı ve üretilme hızından ötürü insan eliyle doğrulanması mümkün olamamaktadır. Dahası rejimler, hükümetler ve etnik kökenler denetimden uzak sahte haberlerin yıkıcı etkisine maruz kalmakta ve bu olumsuz etkilerin minimuma indirgenmesi için yeni teknolojilere ihtiyaç duyulmaktadır. Son yıllarda farklı doğal dil işleme görevlerinde ön-eğitimli modeller başarı ile kullanılmaktadır. Derin sinir ağı ve kavramsal kelime gömülmelerinin birlikte kullanılması durumunda hangi sınıflandırıcının daha verimli olduğu hususu yeterince net değildir. Bu noktada kapsamlı ve karşılaştırmalı çalışmanın eksikliğinden ötürü Global Vektörler (GloVe) gömülme katmanının sağladığı bağlamsal temsiller ile dört farklı sınıflandırıcı deneysel sürece dahil edilmiştir. GLoVe katmanından sonra Çok Katmanlı Algılayıcı (Multi-Layer Perceptron, MLP), Uzun Ömürlü Kısa Dönem Bellek Ağı (Long-Short Term Memory, LSTM), Yinelemeli Sinir Ağları (Recurrent Neural Network, RNN) ve Evrişimsel Sinir ağları kullanılmıştır (Convolutional Neural Network, CNN). Deneysel çalışmalarda açık erişimli COVID-19 isimli sahte haber tespit veri seti kullanılmış, başarımı çeşitli performans metrikleri ile hesaplanmıştır. En yüksek başarım %91 ile LSTM tarafından rapor edilmiştir. Ön-eğitimli kelime gömülmelerinin farklı sinirsel ağlardan bağımsız olarak yüksek bir hassasiyetle sahte haberlerin tespitinde kullanılabileceğini gösteren umut verici sonuçlar sunulmuştur.

The spine is composed of pieces of bone called vertebrae that lie between the skull and the tailbone. Various medical conditions can affect the spine. In this study, two types of degenerative diseases, scoliosis, and spondylolisthesis, were studied. Deep AI architectures have recently enabled further disease diagnosis innovation using medical images. Various traditional and deep learning studies use medical images for disease diagnosis in the literature. This study aims to classify spine X-ray images according to three possible conditions (Normal, Scoliosis, and Spondylolisthesis) and to exploit the potential of these X-ray images to detect possible diseases occurring in the spine. The performance of deep learning models and optimization algorithms used in this process was evaluated. The study uses a data set created and/or analyzed during an existing study. This data set consists of images that belong to three different classes: scoliosis, spondylolisthesis, or x-ray images of normal (i.e. healthy) individuals. A total of 338 spine X-ray images, 188 scoliosis images, 79 spondylolisthesis images, and 71 normal images. Six different deep-learning architectures have been used in the study. These architectures are Alexnet, GoogLeNet, ResNet-18, ResNet-50, ResNet-101, and EfficientNet-bo. While working on these deep architectures, each model has been evaluated using different optimization algorithms. These optimization algorithms are RmsProp, SGDM, and Adam. According to the classification processes, the deep learning model with the highest accuracy value was Alexnet, and the optimization algorithm used with it, Sgdm (99.01%), and the training time lasted 38 seconds. According to the classification processes, the deep learning model with the fastest completion time (30 seconds) was Alexnet and the optimization algorithm used with it was RmsProp. An accuracy rate of 98.02% has been obtained in the training of this model.

Large Language Models (LLMs) have shown a strong performance across various tasks but still face challenges in automatic text summarization. While they are effective in capturing semantic patterns from large corpora, they typically lack mechanisms for encoding structural relationships between sentences or paragraphs. Their high hardware requirements and limited analysis as to processing efficiency further constrain their applicability. This paper proposes a framework employing the Graph Independent Set approach to extract the essence of textual graphs and address the limitations of LLMs. The framework encapsulates nodes and relations into structural graphs generated through Natural Language Processing (NLP) techniques based on the Maximum Independent Set (MIS) theory. The incorporation of graph-derived structural features enables more semantically cohesive and accurate summarization outcomes. Experiments on the Document Understanding Conference (DUC) and Cable News Network (CNN)/DailyMail datasets are conducted with different summary lengths to evaluate the performance of the framework. The proposed method provides up to a 41.05% (Recall-Oriented Understudy for Gisting Evaluation, ROUGE-2 F1) increase in summary quality and a 60.71% improvement in response times on models such as XLNet, Pegasus, and DistilBERT. The proposed framework enables more informative and concise summaries by embedding structural relationships into LLM-driven semantic representations, while reducing computational costs. In this study, we explore whether integrating MIS-based graph filtering with LLMs significantly enhances both the accuracy and efficiency of extractive text summarization.

There is growing interest in studies on text classification as a result of the exponential increase in the amount of data available. Many studies have been conducted in the field of text clustering, using different approaches. This study introduces Spectral Sentence Clustering (SSC) for text clustering problems, which is an unsupervised method based on graph-partitioning. The study explains how the proposed model proposed can be used in natural language applications to successfully cluster texts. A spectral graph theory method is used to partition the graph into non-intersecting sub-graphs, and an unsupervised and efficient solution is offered for the text clustering problem by providing a physical representation of the texts. Finally, tests have been conducted demonstrating that SSC can be successfully used for text categorization. A clustering success rate of 97.08% was achieved in tests conducted using the TTC-3600 dataset, which contains open-access unstructured Turkish texts, classified into categories. The SSC model proposed performed better compared to a popular k-means clustering algorithm.