Explore the vast range of titles available.

Automatic text summarization (ATS) is a critical area in natural language processing that aims to condense long documents into concise and meaningful summaries. Manual text summarization is a time-consuming, labor-intensive, and costly process. Researchers have been actively working to enhance ATS techniques, focusing on either extractive, abstractive, or hybrid methods. This review explores different summarization techniques, including extractive and abstract approaches, with a focus on recent developments using machine learning and deep learning models. The paper also discusses the challenges in generating high-quality summaries, including coherence, redundancy, and information loss. Finally, this review provides a comprehensive survey for researchers, presenting the various aspects of ATS, including approaches, taxonomy analysis, datasets, evaluation methods, and future research trends. El resumen automático de texto (ATS) es un área crítica en el procesamiento del lenguaje natural que tiene como objetivo condensar documentos largos en resúmenes concisos y significativos. El resumen manual de texto es un proceso costoso, laborioso y que requiere mucho tiempo. Los investigadores han estado trabajando activamente para mejorar las técnicas de ATS, centrándose en métodos extractivos, abstractos o híbridos. Esta revisión explora diferentes técnicas de resumen, incluidos los enfoques extractivos y abstractos, con un enfoque en los desarrollos recientes que utilizan modelos de aprendizaje automático y aprendizaje profundo. El artículo también analiza los desafíos en la generación de resúmenes de alta calidad, incluida la coherencia, la redundancia y la pérdida de información. Finalmente, esta revisión proporciona una encuesta integral para investigadores, presentando los diversos aspectos de ATS, incluidos los enfoques, el análisis de taxonomía, los conjuntos de datos, los métodos de evaluación y las tendencias de investigación futuras.

Text summarization is the process of condensing a long text into a shorter version by maintaining the key information and its meaning. Automatic text summarization can save time and helps in selecting the important and relevant sentences from the document. In extractive summarization techniques, sentences are picked up directly from the source document, whereas in abstractive summarization techniques, new sentences and phrases are generated from original source document. Majority of research is focused on extractive techniques, in the recent years, most of the research is inclined towards abstractive & hybrid text summarization methods. This paper presents comprehensive survey on various works performed for automatic text summarization methods. Detailed study is done on the Extractive & Abstractive techniques & their comparison on different aspects. The paper also discusses the research gaps & challenges that can motivate & help researchers to identify the potential areas for research in this field.

Arabic is one of the most semantically and syntactically complex languages in the world. A key challenging issue in text mining is text summarization, so we propose an unsupervised score-based method which combines the vector space model, continuous bag of words (CBOW), clustering, and a statistically-based method. The problems with multidocument text summarization are the noisy data, redundancy, diminished readability, and sentence incoherency. In this study, we adopt a preprocessing strategy to solve the noise problem and use the word2vec model for two purposes, first, to map the words to fixed-length vectors and, second, to obtain the semantic relationship between each vector based on the dimensions. Similarly, we use a k-means algorithm for two purposes: (1) Selecting the distinctive documents and tokenizing these documents to sentences, and (2) using another iteration of the k-means algorithm to select the key sentences based on the similarity metric to overcome the redundancy problem and generate the initial summary. Lastly, we use weighted principal component analysis (W-PCA) to map the sentences’ encoded weights based on a list of features. This selects the highest set of weights, which relates to important sentences for solving incoherency and readability problems. We adopted Recall-Oriented Understudy for Gisting Evaluation (ROUGE) as an evaluation measure to examine our proposed technique and compare it with state-of-the-art methods. Finally, an experiment on the Essex Arabic Summaries Corpus (EASC) using the ROUGE-1 and ROUGE-2 metrics showed promising results in comparison with existing methods.

Automatic summarization is attracting increasing attention as one of the most promising research areas. This technology has been tried in various real-world applications in recent years and achieved a good response. However, the applicability of conventional evaluation metrics cannot keep up with rapidly evolving summarization task formats and ensuing indicator. After recent years of research, automatic summarization task requires not only readability and fluency, but also informativeness and consistency. Diversified application scenarios also bring new challenges both for generative language models and evaluation metrics. In this review, we analysis and specifically focus on the difference between the task format and the evaluation metrics.

Text Summarization, a vital aspect of natural language processing, aims to condense text while retaining its essential meaning. This process is achieved through extractive and abstractive methods. Deep Learning faces challenges in this domain, including semantic understanding, preservation of meaning, efficient handling of long documents, and ensuring coherence and grammatical correctness. Despite these challenges, deep learning offers advantages such as time saving, facilitating information retrieval, scalability, and content personalization. Also, deep learning faces the risk of losing important details, subjectivity in information selection, difficulty in handling complex texts, and variability in summary quality. Addressing these challenges remains an ongoing focus of research and development in the field of NLP. This paper proposes text summarization approach utilizing deep learning models, namely T5-base, T5-large, BART CNN, and PEGASUS. The methodology involves initial data cleaning and preprocessing of the dataset, followed by exploratory data analysis (EDA) to gain insights into the data. Subsequently, the Rouge and BLUE scores of each model are calculated to assess their summarization performance. After training the models, the Rouge and BLUE scores are re-evaluated to measure their effectiveness in generating summaries. The primary objective is to compare the performance of these models based on their Rouge scores, aiming to identify the model that provides the highest Rouge score, indicative of better summary quality. This study contributes to the advancement of text summarization techniques and provide insights into the effectiveness of various deep learning models in this domain.

With the rapid growth of social media platforms, digitization of official records, and digital publication of articles, books, magazines, and newspapers, lots of data are generated every day. This data is a foundation of information and contains a vast amount of text that may be complex, ambiguous, redundant, irrelevant, and unstructured. Therefore, we require tools and methods that can help us understand and automatically summarize the vast amount of generated text. There are mainly two types of approaches to perform text summarization: abstractive and extractive. In Abstractive Text Summarization, a concise summary is generated by including the salient features of the input documents and paraphrasing documents using new sentences and phrases. While in Extractive Text Summarization, a summary is produced by selecting and combining the most significant sentences and phrases from the source documents. The researchers have given numerous techniques for both kinds of text summarization. In this work, we classify Extractive Text Summarization approaches and review them based on their characteristics, techniques, and performance. We have discussed the existing Extractive Text Summarization approaches along with their limitations. We also classify and discuss evaluation measures and provide the research challenges faced in Extractive Text Summarization.

Day by day, the amount of online and offline text data is growing tremendously from various sources like legal documents, medical documents, news articles, etc. Manual text summarization of large documents is unfeasible and costly because it takes much time and requires more effort. As a consequence, various graph-based text summarization techniques have been designed which provide thoroughly and well-prepared summaries of documents. The problems issues that exist in these techniques are redundancy of data, loss of information and readability. To overcome these problems, we have proposed a textual graph-based extractive text summarization technique called TGETS, for extracting essential information from a single document. In the proposed approach, a graph’s node is denoted as group of sentences in the document and an edge of the graph is represented as an association between two sentences. The summary generation is based on the sum of sentence weight and the average weight of the textual graph. The performance of proposed approach is evaluated on the BBC news articles dataset through the ROUGE-metric ( R 1 and R 2 ). The proposed approach in the range of 100-200 words length summary offers better scores of 19.88%, 38.76%, and 30.73% for R 1 under precision, recall and F 1 -score with respect to the existing PageRank (PR) method. Similarly, for R 2 , the proposed approach exceeds by 32%, 26.99%, and 29.01% for precision, recall, and F 1 -score with respect to existing PageRank (PR) method.

The availability of a tremendous amount of online information bringing about a broad interest in extracting relevant information in a compact and meaningful way, prompted the need for automatic text summarization. Hence, in the proposed system, the automated text summarization has been considered as an extractive single-document summarization problem, and a Cat Swarm Optimization (CSO) algorithm-based approach is proposed to solve it, whose objective is to generate good summaries in terms of content coverage, informative, anti-redundancy, and readability. In this work, input documents are pre-processed first. Then the cat population is initialized, where each individual (cat) in a binary vector is randomly initialized in the search space, considering the constraint. The objective function is then formulated considering different sentence quality measures. The Best Cat Memory Pool (BCMP) is initialized based on the objective function score. After that, individuals are randomly distributed for position updating to perform seeking/tracing mode operations based on the mixture ratio in each iteration. BCMP is also updated accordingly. Finally, an optimal individual is chosen to generate the summary after the last iteration. DUC-2001 and DUC-2002 data sets and ROUGE measures are used for system evaluation, and the obtained results are compared with the various state-of-the-art methods. We have achieved approximately 25% and 5% improvement on ROUGE-1 and ROUGE-2 scores on the datasets over the best existing method mentioned in this paper, revealing the proposed method’s superiority. The proposed system is also evaluated considering the generational distance, CPU processing time, cohesion, and readability factor, reflecting that the system-generated summaries are readable, concise, relevant, and fast. We have also conducted a two-sample t-test, and one-way ANOVA test showing the proposed approach is statistically significant.

The surge in global technological advancements has led to an unprecedented volume of information sharingacross diverse platforms. This information, easily accessible through browsers, has created an overload, making it challenging for individuals to efficiently extract essential content. In response, this paper proposes a hybrid Automatic Text Summarization (ATS) method, combining LexRank and YAKE algorithms. LexRank determines sentence scores, while YAKE calculates individual word scores, collectively enhancing summarization accuracy. Leveraging an unsupervised learning approach, the hybrid model demonstrates a 2% improvement over its base model. To validate the effectiveness of the proposed method, the paper utilizes 5000 Indonesian news articles from the Indosum dataset. Ground-truth summaries are employed, with the objective of condensing each article to 30% of its content. The algorithmic approach and experimental results are presented, offering a promising solution to information overload. Notably, the results reveal a two percent improvement in the Rouge-1 and Rouge-2 scores, along with a one percent enhancement in the Rouge-L score. These findings underscore the potential of incorporating a keyword score to enhance the overall accuracy of the summaries generated by LexRank. Despite the absence of a machine learning model in this experiment, the unsupervised learning and heuristic approach suggest broader applications on a global scale. A comparative analysis with other state-of-the-art text summarization methods or hybrid approaches will be essential to gauge its overall effectiveness.

Due to the exponential overflow of textual information in various fields of knowledge and on the internet, it is very challenging to extract important information or to generate a summary from some multi-document collection in a specific field. With such a gigantic amount of textual content, human text summarization becomes impractical since it is expensive and consumes a lot of time and effort. So, developing automatic text summarization (ATS) systems is becoming increasingly essential . ATS approaches are either extractive or abstractive. The extractive approach is simpler and faster than the abstractive approach. This work proposes an extractive ATS system that aims to extract a small subset of sentences from a large multi-document text. First, the whole text is preprocessed by applying some natural language processing techniques such as sentences segmentation, words tokenization, removal of stop-words, and stemming to provide a structured representation of the original document collection. Based on this structured representation, the ATS problem is formulated as a multi-objective optimization (MOO) problem that optimizes the extracted summary to maintain the coverage of the main text content while avoiding redundant information. Secondly, an evolutionary sparse multi-objective algorithm is developed to solve the formulated large-scale MOO. The output of this algorithm is a set of non-dominated summaries (Pareto front). A novel criterion is proposed to select the target summary from the Pareto front. The proposed ATS system has been examined using (DUC) datasets, and the output summaries have been evaluated using (ROUGE) metrics and compared with the literature.