Text Summarization System Research Articles

Implementation of text summarization on indonesian scientific articles using textrank algorithm with TF-IDF web-based

The development of information technology has significantly changed how information is accessed, necessitating readers to absorb content efficiently and make quick decisions. To address this challenge, this research developed a text summarization system specifically for Indonesian scientific articles using a web-based implementation of the TextRank and TF-IDF algorithms. TextRank was selected for its capability to identify key sentences without requiring training data, while TF-IDF was employed to weight words based on their frequency within the document. The dataset comprised 100 scientific articles in Indonesian from the Unimed Kode Journal, covering the years 2022-2024. The summarization process included several critical stages: text preprocessing, TF-IDF weighting, cosine similarity calculation, and sentence ranking. The resulting summaries were rigorously evaluated by language experts and website specialists using a Likert scale to assess both the quality of the summaries and the usability of the system. The findings demonstrated that the system effectively generated summaries that retained essential information from the original articles, with the highest accuracy observed at a 50% compression rate (88.533%). Additionally, the system achieved good performance at 40% compression (85.133%) and 30% compression (81.26%). The web-based system allows users to input article text and quickly obtain a summary, offering a practical tool for researchers and readers to efficiently comprehend academic content.

Contextual embedded text summarizer system: A hybrid approach

AbstractSelecting crucial sentences from a document is a pivotal task in automatic text summarization systems. Abstractive summarization involves rephrasing key content through advanced natural language techniques, generating a concise, new text conveying critical information. Conversely, extractive summarization reproduces important material from the original text. In the proposed method, a hybrid ensemble approach combines BERTsum for extractive summarization and Longformer2Roberta for abstractive summarization for generating a contextual semantic rich summary for a huge collection of text. These proposed system‐generated summaries were evaluated against reference summaries using the ROUGE package at three rouge levels (Rouge‐1, Rouge‐2, and Rouge‐L). The Proposed contextual embedded hybrid text summarization model has shown significant performance improvement in multiple levels of Rouge score and word mover distance (WMD) of generated summary with a reference summary. The proposed hybrid model demonstrates superior performance over existing state‐of‐the‐art summarizing models on three distinct datasets CNN dataset, WikiSum, and Gigaword dataset. The proposed hybrid model as a text summarizer involves leveraging its capabilities to process longer sequences of text with domain‐specific contextual summaries. This transformers‐based text summarization model has great potential in developing expert systems in various research domains such as health decision support systems, the education sector, customer support chatbots, financial analysis investment recommendations, and financial assistance.

Text Summarization System by Deep Learning & Transfer Model

This paper evaluates recent advances in text summarization strategies, focusing at the strategies developed and deployed in a practical assignment. The project makes use of both abstractive and extractive summarization methods, employing deep gaining knowledge of models such as the Transformer architecture. This paper will discover the comparative overall performance of various models, discuss their deployment, and highlight destiny potentialities for boosting textual content summarization technology.

Encoder-Decoder Transformers for Textual Summaries on Social Media Content

Social media has a leading role to our lives due to radical upgrade of internet and smart technology. It is the primary way of informing, advertising, exchanging opinions and expressing feelings. Posts and comments under each post shape public opinion on different but important issues making social media’s role in public life crucial. It has been observed that people's opinions expressed through social networks are more direct and representative than those expressed in face-to-face communication. Data shared on social media is a cornerstone of research because patterns of social behavior can be extracted that can be used for government, social, and business decisions. When an event breaks out, social networks are flooded with posts and comments, which are almost impossible for someone to read all of them. A system that would generate summarization of social media contents is necessary. Recent years have shown that abstract summarization combined with transfer learning and transformers has achieved excellent results in the field of text summarization, producing more human-like summaries. In this paper, a presentation of text summarization methods is first presented, as well as a review of text summarization systems. Finally, a system based on the pre-trained T5 model is described to generate summaries from user comments on social media.

Towards Trustworthy AI-Enabled Decision Support Systems: Validation of the Multisource AI Scorecard Table (MAST)

The Multisource AI Scorecard Table (MAST) is a checklist tool to inform the design and evaluation of trustworthy AI systems based on the U.S. Intelligence Community’s analytic tradecraft standards. In this study, we investigate whether MAST can be used to differentiate between high and low trustworthy AI-enabled decision support systems (AI-DSSs). Evaluating trust in AI-DSSs poses challenges to researchers and practitioners. These challenges include identifying the components, capabilities, and potential of these systems, many of which are based on the complex deep learning algorithms that drive DSS performance and preclude complete manual inspection. Using MAST, we developed two interactive AI-DSS testbeds. One emulated an identity-verification task in security screening, and another emulated a text-summarization system to aid in an investigative task. Each testbed had one version designed to reach low MAST ratings, and another designed to reach high MAST ratings. We hypothesized that MAST ratings would be positively related to the trust ratings of these systems. A total of 177 subject-matter experts were recruited to interact with and evaluate these systems. Results generally show higher MAST ratings for the high-MAST compared to the low-MAST groups, and that measures of trust perception are highly correlated with the MAST ratings. We conclude that MAST can be a useful tool for designing and evaluating systems that will engender trust perceptions, including for AI-DSS that may be used to support visual screening or text summarization tasks. However, higher MAST ratings may not translate to higher joint performance, and the connection between MAST and appropriate trust or trustworthiness remains an open question.

Toward an efficient extractive Arabic text summarisation system based on Arabic large language models

Toward an efficient extractive Arabic text summarisation system based on Arabic large language models

Improved hybrid text summarization system using deep contextualized embeddings and statistical features

Improved hybrid text summarization system using deep contextualized embeddings and statistical features

FuzzyTP-BERT: Enhancing extractive text summarization with fuzzy topic modeling and transformer networks

In the rapidly evolving field of natural language processing, the demand for efficient automated text summarization systems that not only distill extensive documents but also capture their nuanced thematic elements has never been greater. This paper introduces the FuzzyTP-BERT framework, a novel approach in extractive text summarization that synergistically combines Fuzzy Topic Modeling (FuzzyTM) with the advanced capabilities of Bidirectional Encoder Representations from Transformers (BERT). Unlike traditional extractive methods, FuzzyTP-BERT integrates fuzzy logic to refine topic modeling, enhancing the semantic sensitivity of summaries by allowing a more nuanced representation of word-topic relationships. This integration results in summaries that are not only coherent but also thematically rich, addressing a significant gap in current summarization technology. Extensive evaluations on benchmark datasets demonstrate that FuzzyTP-BERT significantly outperforms existing models in terms of ROUGE scores, effectively balancing topical relevance with semantic coherence. Our findings suggest that incorporating fuzzy logic into deep learning frameworks can markedly improve the quality of automated text summaries, potentially benefiting a wide range of applications in the information overload age.

EASESUM: an online abstractive and extractive text summarizer using deep learning technique

<div align="center"><table width="590" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p class="CM12">Large volumes of information are generated daily, making it challenging to manage such information. This is due to redundancy and the type of data available, most of which needs to be more structured and increases the amount of search time. Text summarization systems are considered a real solution to this vast amount of data because they are used for document compression and reduction. Text summarization keeps the relevant information and eliminates the text's non-relevant parts. This study uses two types of summarizers: Extractive Text summarizers and Abstractive text summarizers. The Text Rank Algorithm was used to implement the Extractive summarizer, while Bi-directional Recurrent Neural Network (RNN) was used to implement the Abstractive text summarizer. To improve the quality of summaries produced, word embedding was also used. For the evaluation of the summarizers, the ROUGE evaluation system was used. ROUGE contrasts summaries created by hand versus those created automatically. ROUGE examination of the produced summary revealed the superiority of human-produced summaries over those generated automatically. For this paper, a summarizer was implemented as a Web Application. The average ROUGE recall score ranging from 30.00 to 60.00 for abstractive summarizer and 0.75 to 0.82 for extractive text showed an encouraging result.</p></td></tr></tbody></table></div>

Text Summarization Based on Semantic Similarity

In the contemporary information age, the sheer volume of textual data poses a significant challenge for efficient comprehension and utilization. This project endeavors to address this challenge by developing a Text Summarization System grounded in semantic similarities. The primary goal is to create a robust and intuitive tool that extracts key information from large textual datasets, offering users a concise and meaningful summary. The proposed system employs advanced Natural Language Processing (NLP) techniques to analyze the semantic relationships within the text. Rather than relying solely on syntactic structures, the model identifies and leverages semantic similarities, such as shared concepts, themes, and contextual relationships, to distill the essential content. This approach enhances the summarization process by ensuring that the generated summaries reflect a deeper understanding of the underlying semantics, thereby capturing the core meaning of the text. Throughout the development of this project, the B.Tech student will delve into the intricacies of semantic analysis, exploring techniques to recognize and prioritize key concepts. The system's effectiveness will be evaluated through rigorous testing on diverse textual datasets, assessing its ability to generate coherent and relevant summaries across various domains. This project not only contributes to the field of NLP but also has practical applications in information retrieval, document summarization, and content curation. By providing an innovative solution to the challenges of information overload, the Text Summarization System based on semantic similarities offers a valuable tool for enhancing efficiency in information processing and decision-making. Index terms Text Summarization, Semantic Similarities, Natural Language Processing (NLP), Semantic Analysis, Information Retrieval, Document Summarization, Content Curation, Information Overload, Decision Making, Textual Data Analysis, Key Concept Recognition, Conceptual Relationships, Syntactic Structures, Semantic Understanding, Textual Datasets Evalution.

Dilated convolution for enhanced extractive summarization: A GAN-based approach with BERT word embedding

Text summarization (TS) plays a crucial role in natural language processing (NLP) by automatically condensing and capturing key information from text documents. Its significance extends to diverse fields, including engineering, healthcare, and others, where it offers substantial time and resource savings. However, manual summarization is a laborious task, prompting the need for automated text summarization systems. In this paper, we propose a novel strategy for extractive summarization that leverages a generative adversarial network (GAN)-based method and Bidirectional Encoder Representations from Transformers (BERT) word embedding. BERT, a transformer-based architecture, processes sentence bidirectionally, considering both preceding and following words. This contextual understanding empowers BERT to generate word representations that carry a deeper meaning and accurately reflect their usage within specific contexts. Our method adopts a generator and discriminator within the GAN framework. The generator assesses the likelihood of each sentence in the summary while the discriminator evaluates the generated summary. To extract meaningful features in parallel, we introduce three dilated convolution layers in the generator and discriminator. Dilated convolution allows for capturing a larger context and incorporating long-range dependencies. By introducing gaps between filter weights, dilated convolution expands the receptive field, enabling the model to consider a broader context of words. To encourage the generator to explore diverse sentence combinations that lead to high-quality summaries, we introduce various noises to each document within our proposed GAN. This approach allows the generator to learn from a range of sentence permutations and select the most suitable ones. We evaluate the performance of our proposed model using the CNN/Daily Mail dataset. The results, measured using the ROUGE metric, demonstrate the superiority of our approach compared to other tested methods. This confirms the effectiveness of our GAN-based strategy, which integrates dilated convolution layers, BERT word embedding, and a generator-discriminator framework in achieving enhanced extractive summarization performance.

Implementasi Algoritma Graf dan Algoritma Genetika pada Peringkasan Single Document

In today's technological advancements, finding information is easier and faster. But not a little information that is not true or commonly referred to as hoaxes. Therefore, information must be obtained from several sources to ensure the accuracy of the information. Automatic Text Summarization System is a system used for text based document summarization. This system can help find the core of a news document, so it does not require much time to read. Researchers use Graph Algorithms and Genetic Algorithms in system development. From the test results obtained by the accuracy of the system produced by the system with manual numbers have a cosine similarity value of 71.21%. This can prove that the system built can be used by users because the results of tests carried out get high accuracy values.

Automatic Text Summarization System for Scientific Papers on the basis of T5 Model, on-the-fly Constructed Corpus, and Citation

Automatic Text Summarization System for Scientific Papers on the basis of T5 Model, on-the-fly Constructed Corpus, and Citation

Automatic text summarisation system for scientific papers on the basis of T5 model, on-the-fly constructed corpus and citations

Automatic text summarisation system for scientific papers on the basis of T5 model, on-the-fly constructed corpus and citations

Automated Abstractive Text Summarization Using Multidimensional Long Short-Term Memory

Text summarization is a technique for condensing and presenting the most crucial information from a larger text in a succinct manner. It is a challenging task for human beings to manually summarize extensive documents due to the time required and cognitive effort involved. The increasing amount of textual data generated in recent years has led to a greater demand for efficient and automated text summarization methods. This study explores the utilization of deep learning and neural network concepts to create an automatic abstractive text summarization system for product reviews using multi-dimensional long short-term memory. Since, reading product reviews can be a time-consuming task, especially given the abundance and diversity of reviews written by numerous individuals. The proposed model was evaluated by comparing the generated summaries to reference summaries using metrics such as Rouge. Further, the performance of the model is compared with the prevalent state-of-the-art techniques. The proposed Multi-Dimensional LSTM model was evaluated using the Amazon food review dataset and compared with four other text summarization methods. The proposed model outperformed the prevalent models in terms of ROUGE-1 and ROUGE-L scores but was slightly outperformed in terms of the ROUGE-2 score.

Automatic Update Summarization by a Multiobjective Number-One-Selection Genetic Approach.

Currently, the explosive growth of the information available on the Internet makes automatic text summarization systems increasingly important. A particularly relevant challenge is the update summarization task. Update summarization differs from traditional summarization in its dynamic nature. While traditional summarization is static, that is, the document collections about a specific topic remain unchanged, update summarization addresses dynamic document collections based on a specific topic. Therefore, update summarization consists of summarizing the new document collection under the assumption that the user has already read a previous summarization and only the new information is interesting. The multiobjective number-one-selection genetic algorithm (MONOGA) has been designed and implemented to address this problem. The proposed algorithm produces a summary that is relevant to the user's given query, and it also contains updates information. Experiments were conducted on Text Analysis Conference (TAC) datasets, and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics were considered to assess the model performance. The results obtained by the proposed approach outperform those from the existing approaches in the scientific literature, obtaining average percentage improvements between 12.74% and 55.03% in the ROUGE scores.

An Extractive Text Summarization Model for Generating Extended Abstracts of Medical Papers in Turkish

The rapid growth of technology has led to an increase in the amount of data available in the digital environment. This situation makes it difficult for users to find the information they are looking for within this vast dataset, making it time-consuming. To alleviate this difficulty, automatic text summarization systems have been developed as a more efficient way to access relevant information in texts compared to traditional summarization techniques. This study aims to extract extended summaries of Turkish medical papers written about COVID-19. Although scientific papers already have abstracts, more comprehensive summaries are still needed. To the best of our knowledge, automatic summarization of academic studies related to COVID-19 in the Turkish language has not been done before. A dataset was created by collecting 84 Turkish papers from DergiPark. Extended summaries of 2455 and 1708 characters were obtained using widely used extractive methods such as Term Frequency and LexRank algorithms, respectively. The performance of the text summarization model was evaluated based on Recall, Precision, and F-score criteria, and the algorithms were shown to be effective for Turkish. The results of the study showed similar accuracy rates to previous studies in the literature.

Textual entailment as an evaluation metric for abstractive text summarization

Automated text summarization systems require to be heedful of the reader and the communication goals since it may be the determining component of whether the original textual content is actually worth reading in full. The summary can also assist enhance document indexing for information retrieval, and it is generally much less biased than a human-written summary. A crucial part while building intelligent systems is evaluating them. Consequently, the choice of evaluation metric(s) is of utmost importance. Current standard evaluation metrics like BLEU and ROUGE, although fairly effective for evaluation of extractive text summarization systems, become futile when it comes to comparing semantic information between two texts, i.e in abstractive summarization. We propose textual entailment as a potential metric to evaluate abstractive summaries. The results show the contribution of text entailment as a strong automated evaluation model for such summaries. The textual entailment scores between the text and generated summaries, and between the reference and predicted summaries were calculated, and an overall summarizer score was generated to give a fair idea of how efficient the generated summaries are. We put forward some novel methods that use the entailment scores and the final summarizer scores for a reasonable evaluation of the same across various scenarios. A Final Entailment Metric Score (FEMS) was generated to get an insightful idea in order to compare both the generated summaries.

TPTS: Text pre-processing Techniques for Sindhi Language

The Internet is a significant source of textual data, with users generating vast amounts of information through social media and news agencies daily. The extraction of meaningful information from large datasets is a challenging and costly process. Text pre-processing is a crucial initial step in any Natural Language Processing (NLP) task, as it can impact the overall performance of the study. The main objective of text pre-processing is to transform unstructured text into a linguistically meaningful (standard form) format, making extracting information for any text-processing task easier. This paper introduces TPTS, a model for text pre-processing in the Sindhi language. TPTS performs essential NLP tasks such as text tokenization, normalization, stop-word removal, stemming, and POS tagging for the Sindhi language. The Sindhi Text Corpus (STC), consisting of 1.5k Sindhi text documents collected from various online news websites, is used for experimentation. The TF-IDF approach is employed to identify high-frequency stop-words in the Sindhi language. Furthermore, a rule-based system tags words with their part of speech in Sindhi input text. The ROUGE evaluation metric is used to assess the effectiveness of the proposed TPTS technique, achieving 89% accuracy on the STC corpus. The Sindhi language is spoken by over 30 million people globally, and the lack of adequate NLP tools and resources limits the development of technology and natural language applications that can benefit Sindhi speakers. The proposed TPTS model can aid in developing such applications, making it beneficial not only for text pre-processing tasks but also for other Sindhi language text-processing tasks such as text summarization, sentiment analysis, speech-processing applications, text mining, and information retrieval systems.

Effective News Text Summarization Techniques

n proposed work, we successfully implemented a news text summarization system using Natural Language Processing (NLP) techniques and the Latent Semantic Analysis (LSA) algorithm. The purpose of our project was to extract important information from a large volume of news articles and present it in a concise and easily understandable manner. To achieve this, we utilized the LSA algorithm, which is known for its ability to capture the underlying semantic structure of text. LSA employs a mathematical model to analyse relationships between words in a document, creating a semantic representation where words with similar contexts are grouped together in a vector space. The LSA-based summarization process involved several steps. First, we pre-processed the news articles by removing stop words, punctuation, and other non-relevant elements. Then, we constructed a term-document matrix, where rows represented words and columns represented documents, with matrix values representing word frequencies. Next, we applied Singular Value Decomposition (SVD) to the term-document matrix. SVD helped reduce the matrix's dimensionality by identifying the most important latent semantic concepts. This resulted in a lower-dimensional representation that captured the essential information. Finally, we identified the most important sentences in the news articles by measuring the cosine similarity between each sentence and the summary. Sentences with the highest cosine similarity scores were selected as summary sentences. The proposed system demonstrated the effectiveness of the LSA algorithm for news text summarization. By capturing the semantic structure of the text, it generated summaries that allowed users to understand the key points of a news article quickly and easily. Our implementation had practical applications for content recommendation systems, news aggregation platforms, and personalized news feeds. However, it is important to acknowledge the limitations of the LSA algorithm. It may struggle with handling idiomatic expressions and can be sensitive to the quality of the input data. These considerations highlight the need for ongoing research and development to enhance the performance and robustness of news text summarization systems.