Text Summarization Techniques Research Articles

Transformer models in text summarization

Abstract. Text summarization represents a core research topic within the realm of natural language processing and is extensively applied across various domains, including journalism, library administration, information gathering, among others. With the development of deep learning, especially the proposed Transformer structure has greatly promoted the development of text summaries. This paper reviews the recent progress in Transformer-based text summarization methods. It begins with an overview of traditional text summarization techniques. The paper then delves into the advantages of Transformer models for text summarization, such as their ability to understand global context, dynamically allocate weights, and accelerate parallel computation. Text summarization models are classified into several types, such as abstraction-based, extraction-based, and those leveraging large language models. Notably, Models like PEGASUS, BERT, and HETFORMER have emerged as leading examples in this field. In addition, the effectiveness, advantages and disadvantages of these models are analyzed.

A Hybrid Text Summarization Technique of Student Open-Ended Responses to Online Educational Surveys

This study introduces a hybrid text summarization technique designed to enhance the analysis of qualitative feedback from online educational surveys. The technique was implemented at the Hellenic Open University (HOU) to tackle the challenges of processing large volumes of student feedback. The TextRank and Walktrap algorithms along with GPT-4o mini were used to analyze student comments regarding positive experiences, study challenges, and suggestions for improvement. The results indicate that students are satisfied with tutor–student interactions but concerns were raised about educational content and scheduling issues. To evaluate the proposed summarization approach, the G-Eval and DeepEval summarization metrics were employed, assessing the relevance, coherence, consistency, fluency, alignment, and coverage of the summaries. This research addresses the increasing demand for effective qualitative data analysis in higher education and contributes to ongoing discussions on student feedback in distance learning environments. By effectively summarizing open-ended responses, universities can better understand student experiences and make informed decisions to improve the educational process.

Social-sum-Mal: A Dataset for Abstractive Text Summarization in Malayalam

Abstractive text summarization techniques for Malayalam language is still in its infancy. The lack of benchmarked datasets for this task is one of the constraints in developing and testing good models. Malayalam has seven nominal case forms, two nominal number forms, and three gender forms. It is subjected to extreme agglutination and inflection. Due to this, the translation of other text summarization datasets to Malayalam may not capture these case forms effectively. Therefore curation of datasets from scratch is highly demanded for specific text-processing applications in Malayalam. This paper introduces a novel dataset designed specifically for advancing the field of automatic abstractive text summarization in Malayalam language. The dataset is curated to address the unique linguistic characteristics of the Malayalam language. It is named as Social-sum-Mal dataset, capable of addressing three different types of summarization tasks- long, extreme, and query-based summarizations. In addition, Social-sum-Mal can be extended for other applications like text classification, multi-document summarization, and question answering. To enhance the dataset transparency, a datasheet is created for Social-sum-Mal. Data accuracy and annotator biases are evaluated using proper testing strategies including Jaccard, cosine, and overlap similarities. The correctness of the dataset is further evaluated by comparing it with a deep-learning-based text summarization model.

Enhancing Knowledge Management in Space Science: Leveraging Knowledge Graphs and Text Summarization Techniques

In recent years Space exploration has evolved from the realm of governments and government agencies and has emerged as a promising industry with diverse stakeholders ranging from R&D labs, Universities and even private firms. There is also increasing international collaboration between governments in this important area with important applications in telecommunications, disaster control, not to mention defence applications. As a result there is a growing amount of data and information making it difficult to navigate through this vast amount of data and information. This paper examines the use of knowledge graphs and text summarization tools fo effective knowledge management in the domain.

Over comparative study of text summarization techniques based on graph neural networks

Due to the enormous content of text available online through emails, social media, and news articles, it has become complicated to summarize the textual information from multiple documents. Text summarization automatically creates a comprehensive description of the document that retains its informative contents through the keywords, where Multi-Document Summarization (MDS) is a productive tool for data accumulation that creates a concise and informative summary from the documents. In order to extract the relevant information from the documents, Graph neural networks (GNNs) is the neural structure that detains the interrelation of the graph by progressing the messages between the graphical nodes. In the current years, the advanced version of GNNs, such as graph attention network (GAN), graph recurrent network, and graph convolutional network (GCN) provides a remarkable performance in text summarization with the advantage of deep learning techniques. Hence, in this survey, graph approaches for text summarization has been analyzed and discussed, where the recent text summarization model based on Deep learning techniques are highlighted. Further, the article provides the taxonomy to abstract the design pattern of Neural Networks and conducts a comprehensive of the existing text summarization model. Finally, the review article enlists the future direction of the researcher, which would motivate the enthusiastic and novel contributions in text summarizations.

Extractive Arabic Text Summarization Using PageRank and Word Embedding

Research on graph-based automatic text summarization for Arabic, the official language of 26 nations with over 200 million speakers, as well as other prevalent languages, has recently increased due to the ability of these approaches to handle linguistic peculiarities such as complex morphological linkages. The present paper proposes a graph-based extractive Arabic text summarization (GEATS) technique that employs word embedding and PageRank algorithms for feature extraction and sentence ordering. The efficiency of the GEATS approach versus the state-of-the-art methods is analyzed based on the quality of the produced summaries over the F-measure values. The findings indicated that it outperformed the nearest alternative by an advantage of over 7.5%.

Text Summarization and Document summarization using NLP

Background: Automatic text summarization has become increasingly crucial in navigating the ever- growing ocean of textual information. This research delves into exploring the potential of Natural Language Processing (NLP) techniques for creating efficient and informative summaries. We implemented and evaluated models based on Long Short-Term Memory (LSTM) networks, Sequence-to-Sequence (Seq2Seq) architectures, and transformer-based approaches. By leveraging these powerful algorithms, we aimed to generate concise summaries that capture the essence of the original text. The evaluation highlighted the strengths and limitations of each approach, showcasing the potential of NLP for text summarization while acknowledging the remaining challenges. This research not only contributes to the ongoing discussion on textsummarization techniques but also opens doorsfor further exploration, including integrating domain-specific knowledge, personalizing summaries based on user preferences, and applying these techniques to real-world information overload situations. Ultimately, this work underscores the promise of NLP-driven text summarization in facilitating efficient information access, comprehension, and utilization across various domains. Keywords: Natural Language Processing(NLP), Automatic Text Summarization, Real-world applications.

A survey on audio analysis: Text characterization and summarization

The integration of cutting-edge natural language processing (NLP) technology for smooth audio-to-text conversion and summarization is examined in this survey. Utilizing Facebook’s BART model for succinct summaries and Google’s Speech-to-Text API for precise transcription. The report highlights the value of sophisticated summarization models and precise transcription. It talks about how the system can be used in a variety of fields, such as podcast and video transcript generation, automated meeting transcription and summarization, content indexing and search, and more. In addition to addressing issues like context preservation and bias reduction, the survey assesses relevant research on text generation, LSTM networks, and summarization techniques. Overall, by incorporating state-of-the-art technology, this study advances the processing of audio content and eventually makes it easier to extract valuable information.

Developing Gujarati Article Summarization Utilizing Improved Page-Rank System

This research delves deep into the domain of Gujarati text summarization, where we employ an improved version of the PageRank algorithm to enhance both efficiency and accuracy. The study is meticulously structured around a comprehensive comparative analysis, juxtaposing our innovative approach against well-established methods like frequency-based summarization, TF-IDF, and LexRank. Through our rigorous investigation, we unveil compelling findings that showcase the superior performance of the enhanced PageRank algorithm, delivering summaries that are not only more concise but also contextually relevant, thus retaining the inherent linguistic intricacies characteristic of Gujarati. This exploration signifies a significant leap forward in the realm of text summarization techniques for Gujarati, carrying broad implications for bolstering information retrieval capabilities and advancing natural language processing functionalities within this linguistic domain.

Indonesian News Text Summarization Using MBART Algorithm

Purpose: Technology advancements have led to the production of a large amount of textual data. There are numerous locations where one can find textual information sources, including blogs, news portals, and websites. Kompas, BBC, Liputan 6, CNN, and other news portals are a few websites that offer news in Indonesian. The purpose of this study was to explore the effectiveness of using mBART in text summarization for Bahasa Indonesia.Methods: This study uses mBART, a transformer architecture, to perform fine-tuning to generate news article summaries in Bahasa Indonesia. Evaluation was conducted using the ROUGE method to assess the quality of the summaries produced.Results: Evaluation using the ROUGE metric showed better results, with ROUGE-1 of 35.94, ROUGE-2 of 16.43, and ROUGE-L of 29.91. However, the performance of the model is still not optimal compared to existing models in text summarization for another language.Novelty: The novelty of this research lies in the use of mBART for text summarization, specifically adapted for Bahasa Indonesia. In addition, the findings also contribute to understanding the challenges and opportunities of improving text summarization techniques in the Indonesian context.

Long Text Summarization and Key Information Extraction in a Multi-Task Learning Framework

Abstract In the context of the rapid advancement of big data and artificial intelligence, there has been an unprecedented surge in text-based information. This proliferation necessitates the development of efficient and accurate techniques for text summarization. This paper addresses this need by articulating the challenges associated with text summarization and key information extraction. We introduce a novel model that integrates multi-task learning with an attention mechanism to enhance the summarization and extraction of long texts. Furthermore, we establish a loss function for the model, calibrated against the discrepancy observed during the training phase. Empirical evaluations were conducted through simulated experiments after pre-processing the data via the proposed extraction model. These evaluations indicate that the model achieves optimal performance in the iterative training range of 55 to 65. When benchmarked against comparative models, our model demonstrates superior performance in extracting long text summaries and key information, evidenced by the metrics on the Daily Mail dataset (mean scores: 40.19, 16.42, 35.48) and the Gigaword dataset (mean scores: 34.38, 16.21, 31.38). Overall, the model developed in this study proves to be highly effective and practical in extracting long text summaries and key information, thereby significantly enhancing the efficiency of processing textual data.

Integration of Transformer Model Text Summarization and Text-to-Speech in Helping Document Understanding in the Bukudio Application

The need for effective, accurate and precise understanding of information will provide optimization of the decision-making process, increase knowledge and quality of life. Understanding information in relation to the document summarization process, if done manually, sometimes takes quite a long time. Text summarization techniques which are useful as document summarizers have been developed and applied to various things such as summarizing important documents, news texts or customer feedback. In this article, text summarization using the text rank method and transformer modeling integrated with text to speech techniques is developed in the Bukudio application, which is an application that provides audio versions of book documents in the application database. Based on the test results, the evaluation process was carried out using the Rouge method and gave the best results in calculating the Rouge 1 overlap monogram resulting in 0.523 for the F1 Score value, 0.434 for the precision value and 0.659 for the recall value. This research will be developed using other methods so that not only files in PDF document format can be processed, but other EPUB (Electronic Publication) files.

Evaluation of text summarization techniques in healthcare domain: Pharmaceutical drug feedback

Text summarization techniques offer a way to address the significant challenges faced by clinicians and researchers due to the exponential growth of information in healthcare on the internet. By condensing lengthy text into concise summaries, these techniques facilitate faster, easier, and convenient access to relevant information. This is particularly beneficial in use cases such as online user feedback/reviews about drugs, where valuable insights can be obtained that extend beyond clinical trials and observational studies. This paper comprehensively evaluates six widely used text summarization techniques (LSA, Luhn’s Method, Text Rank, T5 Transformer, and Kullback-Leibler, BERT) in extracting key insights, themes and patterns about drugs from online drug reviews. The evaluation considers both quantitative and qualitative aspects, focusing on their applicability to the challenging medical terminology, which is known for its inherent intricacies and complexities. The findings of this study showed the performance of text summarization techniques using metrics such as F1 score, Recall, and Precision, focused on the unigram, bigram, and trigram overlap between the generated text summaries and the reference summaries, utilizing the ROUGE-1, ROUGE-2, and ROUGE-L evaluation methods. It is shown that results showed TextRank to be the most effective text summarization method followed by BERT when working with Medical Terminology in Healthcare & Biomedical Informatics, given its complex hierarchy and extensive vocabulary of medical terms.

Extractive social media text summarization based on MFMMR-BertSum

The advancement of computer technology has led to an overwhelming amount of textual information, hindering the efficiency of knowledge intake. To address this issue, various text summarization techniques have been developed, including statistics, graph sorting, machine learning, and deep learning. However, the rich semantic features of text often interfere with the abstract effects and lack effective processing of redundant information. In this paper, we propose the Multi-Features Maximal Marginal Relevance BERT (MFMMR-BertSum) model for Extractive Summarization, which utilizes the pre-trained model BERT to tackle the text summarization task. The model incorporates a classification layer for extractive summarization. Additionally, the Maximal Marginal Relevance (MMR) component is utilized to remove information redundancy and optimize the summary results. The proposed method outperforms other sentence-level extractive summarization baseline methods on the CNN/DailyMail dataset, thus verifying its effectiveness.

Automatic Short Text Summarization Techniques in Social Media Platforms

The rapid expansion of social media platforms has resulted in an unprecedented surge of short text content being generated on a daily basis. Extracting valuable insights and patterns from this vast volume of textual data necessitates specialized techniques that can effectively condense information while preserving its core essence. In response to this challenge, automatic short text summarization (ASTS) techniques have emerged as a compelling solution, gaining significant importance in their development. This paper delves into the domain of summarizing short text on social media, exploring various types of short text and the associated challenges they present. It also investigates the approaches employed to generate concise and meaningful summaries. By providing a survey of the latest methods and potential avenues for future research, this paper contributes to the advancement of ASTS in the ever-evolving landscape of social media communication.

A hybrid drug named entity recognition framework for real time pubmed data using deep learning and text summarization techniques

A hybrid drug named entity recognition framework for real time pubmed data using deep learning and text summarization techniques

EXPLORING THE USE OF NLP TO AUTO-ALIGN MASTER SCHEDULES WITH SUPERINTENDENT’S LOOK-AHEADS IN CONSTRUCTION PROJECTS

In construction, there has always been a painful disconnect in reconciling parameters set in the master plan to what is actually happening in real-time on a project against the superintendent’s look-ahead. In the absence of an integrated solution, superintendents spend hours manually attempting to connect the master schedule with the look-ahead tasks written on trailer walls and sticky notes using papers or spreadsheets. This leads to frequent human error, poor communication between field and office and missing jobsite information created by the realities of weekly re-planning on-site. To address these inefficiencies, this paper explores the use of natural language processing (NLP) on both long-term (master) schedule and short-term (lookahead) plans to automatically learn and map their activities and tasks against one another. Using preliminary results from several commercial building projects, the potential of using an NLP seq2seq model as an Extractive and Abstractive Text Summarization technique is discussed in detail.

ASSESSING THE EFFICACY OF LSTM, TRANSFORMER, AND RNN ARCHITECTURES IN TEXT SUMMARIZATION

The need for efficient and effective techniques for automatic text summarization has become increasingly critical with the exponential growth of textual data in different domains. Summarizing long texts into short summaries facilitates a quick understanding of the key information contained in the documents. In this paper, we evaluate various architectures for automatic text summarization using the TEDx dataset, a valuable resource consisting of a large collection of TED talks with rich and informative speech transcripts. Our research focuses on evaluating the performance of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN) and Transformer architectures for automatic text summarization. We measure the accuracy of each model by comparing the generated summaries with human-written summaries. The findings show that the Transformer model achieves the highest accuracy, followed closely by the GRU model. However, LSTM, RNN exhibit relatively lower accuracies. We also investigate the trade-off between accuracy and conciseness in summarization. Our study reveals that the Transformer model succeeds in producing accurate and concise summaries, albeit at a higher computational cost. On the other hand, the GRU model strikes a desirable balance between accuracy and conciseness, making it a suitable choice. Overall, this research provides valuable insights into the effectiveness of different architectures for automatic text summarization and highlights the superiority of the Transformer and GRU models in this area.

Hybrid Optimization Based Hindi Document Summarization Using Deep Learning Technique

The proliferation of textual information today is a result of the internet's recent development, which is widely accessible to anybody, at any time. Generally speaking, several Natural Language Processing (NLP) techniques can be used to analyze the textual information that is offered on the basis of text documents. In recent years, various text summarization techniques have been implemented in English text documents but a little amount of work is carried out in Hindi text documents summarization. In this research investigation, the Coot Remora Optimization (CRO) technique based on Deep Recurrent Neural Network (DRNN) is used to summarize Hindi documents. Here, the CRO algorithm is used to train the DRNN, which is used to compute the sentence scores.The highest scored sentences are going to included in the summary. When compared to recent optimization algorithmic techniques, such as MCRMR-SSO, Graph-based_PSO, Genetic Algorithms (GA), and Political Elephant Herding Optimization (PEHO) based Deep Long Short Term Memory (DLSTM) algorithm, the developed method is shown to be superior. Additionally, three evaluation metrics such as precision, recall, f-measure are used to analyze the performance of the CRO based DRNN technique and obtained high performance.

Marathi Text Summarization using Extractive Technique

Multilingualism has played a key role in India, where people speak and understand more than one language. Marathi, as one of the official languages inMaharashtra state, is often used in sources such as newspapers or blogs. However, manually summarizing bulky Marathi paragraphs or texts for easy comprehension can be challenging. To address this, text summarization becomes essential to make large documents easily readable and understandable. This research article focuses on single document text summarization using the Natural Language Processing (NLP) approach, a subfield of Artificial Intelligence. Automatic text summarization is employed to extract relevant information in a concise manner. Information Extraction is particularly useful when summarizing documents consisting of multiple sentences into three or four sentences. While extensive research has been conducted on English Text Summarization, the field of Marathi document summarization remains largely unexplored. This research paper explores extractive text summarization techniques specifically for Marathi documents, utilizing the LexRank algorithm along with Genism, a graph-based technique, to generate informative summaries within word limit constraints. The experiment was conducted on the IndicNLP Marathi news article dataset, resulting in 78% precision, 72% recall, and 75% F-measure using the frequency-based method, and 78% precision, 78% recall, and 78% F-measure using the Lex Rank algorithm.