Text Summarization Research Articles

Fine‐Tuned Pretrained Transformer for Amharic News Headline Generation

ABSTRACTAmharic is one of the under‐resourced languages, making news headline generation particularly challenging due to the scarcity of high‐quality linguistic datasets necessary for training effective natural language processing models. In this study, we fine‐tuned the small check point of the T5v1.1 model (t5‐small) to perform Amharic news headline generation with an Amharic dataset that is comprised of over 70k news articles along with their headline. Fine‐tuning the model involves dataset collection from Amharic news websites, text cleaning, news article size optimization using the TF‐IDF algorithm, and tokenization. In addition, a tokenizer model is developed using the byte pair encoding (BPE) algorithm prior to feeding the dataset for feature extraction and summarization. Metrics including Rouge‐L, BLEU, and Meteor were used to evaluate the performance of the model and a score of 0.5, 0.24, and 0.71, respectively, was achieved on the test partition of the dataset that contains 7230 instances. The results were good relative to result of the t5 model without fine‐tuning, which are 0.1, 0.03, and 0.14, respectively. A postprocessing technique using a rule‐based approach was used for further improving summaries generated by the model. The addition of the postprocessing helped the system to achieve Rouge‐L, BLEU, and Meteor scores of 0.72, 0.52, and 0.81, respectively. The result value is relatively better than the result achieved by the nonfine‐tuned T5v1.1 model and the result of previous studies report on abstractive‐based text summarization for Amharic language, which had a 0.27 Rouge‐L score. This contributes a valuable insight for practical application and further improvement of the model in the future by increasing the article length, using more training data, using machine learning–based adaptive postprocessing techniques, and fine‐tuning other available pretrained models for text summarization.

Leveraging Language Models and Automatic Summarization in Online Programming Learning Environments

Leveraging Language Models and Automatic Summarization in Online Programming Learning Environments

Comprehensive Survey of Abstractive Text Summarization Techniques

Text summarization using pre-trained encoders has become a crucial technique for efficiently managing large volumes of text data. The rise of automatic summarization systems addresses the need to process ever-increasing data while meeting user-specific requirements. Recent scientific research highlights significant advancements in abstractive summarization, with a particular focus on neural network-based methods. A detailed review of various neural network models for abstractive summarization identifies five key components essential to their design: encoder-decoder architecture, mechanisms, training strategies and optimization algorithms, dataset selection, and evaluation metrics. Each of these elements is pivotal in enhancing the summarization process. This study aims to provide a thorough understanding of the latest developments in neural network-based abstractive summarization models, offering insights into the evolving field and underscoring the associated challenges. Qualitative analysis using a concept matrix reveals common design trends in contemporary neural abstractive summarization systems. Notably, BERT-based encoder-decoder models have emerged as leading innovations, representing the most recent progress in the field. Based on the insights from this review, the study recommends integrating pre-trained language models with neural network techniques to achieve optimal performance in abstractive summarization tasks. As the volume of online information continues to surge, the field of automatic text summarization has garnered significant attention within the Natural Language Processing (NLP) community. Spanning over five decades, researchers have approached this problem from diverse angles, exploring various domains and employing a multitude of paradigms. This survey aims to delve into some of the most pertinent methodologies, focusing on both single-document and multiple-document summarization techniques, with a particular emphasis on empirical methods and extractive approaches. Additionally, the survey explores promising strategies that target specific intricacies of the summarization task. Notably, considerable attention is dedicated to the automatic evaluation of summarization systems, recognizing its pivotal role in guiding future research endeavors.

A Brief Survey on Safety of Large Language Models

Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) and have been widely adopted in various applications such as machine translation, chatbots, text summarization, and so on. However, the use of LLMs has raised concerns about their potential safety and security risks. In this survey, we explore the safety implications of LLMs, including ethical considerations, hallucination, and prompt injection. We also discuss current research efforts to mitigate these risks and identify areas for future research. Our survey provides a comprehensive overview of the safety concerns related to LLMs, which can help researchers and practitioners in the NLP community develop more safe and ethical applications of LLMs.

Text summarization based on semantic graphs: an abstract meaning representation graph-to-text deep learning approach

Nowadays, due to the constantly growing amount of textual information, automatic text summarization constitutes an important research area in natural language processing. In this work, we present a novel framework that combines semantic graph representations along with deep learning predictions to generate abstractive summaries of single documents, in an effort to utilize a semantic representation of the unstructured textual content in a machine-readable, structured, and concise manner. The overall framework is based on a well defined methodology for performing semantic graph parsing, graph construction, graph transformations for machine learning models and deep learning predictions. The employed semantic graph representation focuses on using the model of abstract meaning representation. Several combinations of graph construction and graph transformation methods are investigated to specify the most efficient of them for the machine learning models. Additionally, a range of deep learning architectures is examined, including a sequence-to-sequence attentive network, reinforcement learning, transformer-based architectures, and pre-trained neural language models. In this direction, a semantic graph representation of an original text is extracted, and then the present framework formulates the problem as a graph-to-summary learning problem to predict a summary of an original text. To the best of our knowledge, this formulation of graph-to-summary predictions in abstractive text summarization, without other intermediate steps in the machine learning phase, has not been presented in the relevant literature. Another important contribution is an introduction of a measure for assessing the factual consistency of the generated summaries in an effort to provide a qualitative evaluation. To assess the framework, an extensive experimental procedure is presented that uses popular datasets to evaluate key aspects of the proposed approach. The obtained results exhibit promising performance, validating the robustness of the proposed framework.

SATS: simplification aware text summarization of scientific documents.

Simplifying summaries of scholarly publications has been a popular method for conveying scientific discoveries to a broader audience. While text summarization aims to shorten long documents, simplification seeks to reduce the complexity of a document. To accomplish these tasks collectively, there is a need to develop machine learning methods to shorten and simplify longer texts. This study presents a new Simplification Aware Text Summarization model (SATS) based on future n-gram prediction. The proposed SATS model extends ProphetNet, a text summarization model, by enhancing the objective function using a word frequency lexicon for simplification tasks. We have evaluated the performance of SATS on a recently published text summarization and simplification corpus consisting of 5,400 scientific article pairs. Our results in terms of automatic evaluation demonstrate that SATS outperforms state-of-the-art models for simplification, summarization, and joint simplification-summarization across two datasets on ROUGE, SARI, and CSS1 . We also provide human evaluation of summaries generated by the SATS model. We evaluated 100 summaries from eight annotators for grammar, coherence, consistency, fluency, and simplicity. The average human judgment for all evaluated dimensions lies between 4.0 and 4.5 on a scale from 1 to 5 where 1 means low and 5 means high.

Summary Augmenter: A Text Augmentation Framework to Improve Summarization Quality

Data augmentation in Natural Language Processing (NLP) faces various challenges that hinder its widespread adoption, unlike its ever-present usage in the field of vision. It is even more the case for the text summarization task where one should focus on both article and summary. In this paper, we review the effect of back translation augmentation, present the diverse beam search decoding strategy, and masking as a method to generate synthetic data for text summarization. The approaches will be evaluated by ROUGE score, novelty, summary length, and GPT-4 to analyze their effectiveness. Our proposed framework presents multiple combinations of back translation and masking for articles, along with diverse augmentation for summaries. Although applicable to networks of any size, we decided to use BART-large, a relatively smaller model, in order to conduct a larger number of experiments. The experiments demonstrated superior performance across all specified metrics when compared to fine-tuning BART-large on the CNN/Dailymail dataset. Specifically, we showed a significant improvement in novelty; 158% and 56% increase rate for bigrams and unigrams, respectively. It could eliminate some copyright concerns around generating content similar to human writing. Additionally, the GPT-4 assessment indicates that models trained using the augmentation technique tend to capture important information more effectively than the baseline model.

Comparative Analysis of Semantic and Syntactic Approaches in Automatic Text Summarization: A Comprehensive Review and Evaluation

It is very important to use automatic text summarization to turn long chunks of text into short, useful snippets. In the area of automatic text summary, this study looks at both semantic and syntactic methods in great detail. Using methods like natural language understanding and semantic processing, semantic approaches try to get meaning and context from writing. These methods try to get at the main ideas and connections in the material so that recaps can be more complex and full of information. Syntactic methods, on the other hand, focus on the structure of language, such as grammar and syntax. To find important sentences and phrases for summarization, methods like sentence extraction based on grammar patterns and grammatical analysis are used. Syntactic methods often make sure that the outlines they create are grammatically right and make sense. This review compares both methods in a number of areas, such as how well they capture the spirit of the original text, how well the generated snippets make sense, how quickly they can be produced, and how well they can be used with different types of material. The pros and cons of each method are shown in comparative studies, which help us understand how they can be used in various areas and situations. Recent improvements that combine semantic and syntactic methods have also shown promise in making automatic recaps better overall and more helpful. The goal of these mixed methods is to make reports that are both verbally and semantically correct by using the benefits of both semantic understanding and syntactic structure. This review gives us a more complete picture of how automatic text summarization methods are changing. It stresses how important it is to look at both semantic and syntactic approaches in order to make the field move forward and create better and more advanced summary systems.

Automatic summarization model based on clustering algorithm

Extractive document summary is usually seen as a sequence labeling task, which the summary is formulated by sentences from the original document. However, the selected sentences usually are high redundancy in semantic space, so that the composed summary are high semantic redundancy. To alleviate this problem, we propose a model to reduce the semantic redundancy of summary by introducing the cluster algorithm to select difference sentences in semantic space and we improve the base BERT to score sentences. We evaluate our model and perform significance testing using ROUGE on the CNN/DailyMail datasets compare with six baselines, which include two traditional methods and four state-of-art deep learning model. The results validate the effectiveness of our approach, which leverages K-means algorithm to produce more accurate and less repeat sentences in semantic summaries.

Hybrid Summarization of Medical Records for Predicting Length of Stay in the Intensive Care Unit

Electronic health records (EHRs) are a critical tool in healthcare and capture a wide array of patient information that can inform clinical decision-making. However, the sheer volume and complexity of EHR data present challenges for healthcare providers, particularly in fast-paced environments such as intensive care units (ICUs). To address this problem, the automatic summarization of the main problems of patients from daily progress notes can be extremely helpful. Furthermore, by accurately predicting ICU patients’ lengths of stay (LOSs), resource allocation and management can be optimized, allowing for a more efficient flow of patients within the healthcare system. This work proposes a hybrid method to summarize EHR notes and studies the potential of these summaries together with structured data for the prediction of LOSs of ICU patients. Our investigation demonstrates the effectiveness of combining extractive and abstractive summarization techniques with a concept-based method combined with a text-to-text transfer transformer (T5), which shows the most promising results. By integrating the generated summaries and diagnoses with other features, our study contributes to the accurate prediction of LOSs, with a support vector machine emerging as our best-performing classifier with an accuracy of 77.5%, surpassing existing systems and highlighting the potential for optimal allocation of resources within ICUs.

DEVELOPING A COMPREHENSIVE LIVE NEWS AGGREGATOR: INTEGRATING REAL-TIME DATA SOURCES FOR ENHANCED INFORMATION ACCESSIBILITY

Abstract—News Aggregator is simply an online software which collects new stories and events around the world from various sources all in one place. News aggregator plays a very important role in reducing time consumption, as all of the news that would be explored through more than one website will be placed only in a single location. Also, summarizing this aggregated content absolutely will save reader’s time. A proposed technique used called the TextRank algorithm that showed promising results for summarization. This paper presents the main goal of this project which is developing a news aggregator able to aggregate relevant articles of a certain input keyword or key-phrase. Summarizing the relevant articles after enhancing the text to give the reader understandable and efficient summary. Index Term : News aggregator; text summarization; text enhancement; TextRank algorithm

Do Code Summarization Models Process Too Much Information? Function Signature May Be All That Is Needed

With the fast development of large software projects, automatic code summarization techniques, which summarize the main functionalities of a piece of code using natural languages as comments, play essential roles in helping developers understand and maintain large software projects. Many research efforts have been devoted to building automatic code summarization approaches. Typical code summarization approaches are based on deep learning models. They transform the task into a sequence-to-sequence task, which inputs source code and outputs summarizations in natural languages. All code summarization models impose different input size limits, such as 50 to 10,000, for the input source code. However, how the input size limit affects the performance of code summarization models still remains under-explored. In this article, we first conduct an empirical study to investigate the impacts of different input size limits on the quality of generated code comments. To our surprise, experiments on multiple models and datasets reveal that setting a low input size limit, such as 20, does not necessarily reduce the quality of generated comments. Based on this finding, we further propose to use function signatures instead of full source code to summarize the main functionalities first and then input the function signatures into code summarization models. Experiments and statistical results show that inputs with signatures are, on average, more than 2 percentage points better than inputs without signatures and thus demonstrate the effectiveness of involving function signatures in code summarization. We also invite programmers to do a questionnaire to evaluate the quality of code summaries generated by two inputs with different truncation levels. The results show that function signatures generate, on average, 9.2% more high-quality comments than full code.

Faster (Multi) Document Summarization using PRIMERA & PEGASUS

Text summarization is super important when it comes to dealing with loads of information that's available on the internet and in archives. Trying to summarize all that stuff manually would be impossible because there's just so much of it. That's why automatic summarization techniques have become a big deal. These techniques basically condense documents by picking out the most important ideas. At first, people were mainly focused on summarizing individual documents, but now the latest research is all about summarizing groups of documents. In our study, we've come up with this cool new way to combine the Primera and Pegasus models. This combo not only improves the quality of the summaries, but it also speeds up the whole process. And guess what? We can even summarize multiple documents at the same time thanks to some batch processing techniques that make everything run smoother.

Implementasi Tunelling pada Perancangan Sistem Peringkasan dan Klasifikasi Berita Otomatis menggunakan Textrank dan KNN

News summarization is very important in the news analysis process. However, in the summarization process, there are often obstacles such as the large number of news stories and the need for news classification. This research aims to build a simple web-based system that can be used to summarize and classify news which will be very useful in the news analysis process. The proposed summarization method is Textrank, and the news classification method that will be used is KNN. This system is expected to provide an automatic summarization function to make it easier to analyze news content. The data that will be used as the basis for classification modeling is sports news in 3 months, and the classification that will be used to determine whether the news includes sports news in three branches, namely football, rackets or basketball. Testing of the summarization model using textrank was carried out by applying ROUGE-1 and ROUGE-2, with results of 0.79 and 0.67. Meanwhile, testing the classification model using KNN with k=3 and k=5 is 0.9866 and 0.9666 so k=3 will be used. This system will be built using the web scrapping library, textrank, stopword from PySastrawi, scikit-learn for the classification module using the KNN algorithm, and ngrok for publishing web-based applications. By using ngrok, we can expose the application through internet with a temporary public url without hosting required

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

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

Objective and neutral summarization of customer reviews

Opinion mining aims to detect and extract relevant information from large quantity of customer reviews. Automatic opinion summarization then seeks to create a consensual point of view often oriented toward the main sentiment of clients to render their experience. Although factual information is valuable for companies to understand what works or not in their products, summarization approaches that convey objectivity, and constructive feedback from customer reviews have yet to be explored. We propose an adversarial multi-task learning model for document summarization to address this new issue. Our algorithm combines an autoencoder for document summarization with a gradient reversal layer to learn independent representations of subjective and sentiment-based material. We assess and compare our method on the Amazon product review dataset where we introduce an original evaluation dataset for objective summarization. We further completed the analysis with neutrality and objectivity metrics. This study demonstrates that the generated summaries carry out relevant and objective content but also emphasize the importance of various processes and layers in multi-task learners to control the information effectively.

Real Text Summarizer Extension

The advent of the World Wide Web, with a wealth of information, required easily accessible information, so databases and search engines were created to access that information. This information system is updated whenever new data is generated, since the volume of electronic information rises everyday, and no one can promise that manual analysis will provide the full picture anymore. Accordingly, the need for automated summarization systems is high. Automatic summary reads few papers as input and produces a summarized version and hence saves both information and time. The research was performed in a single paper and produced multiple papers. In this report, we are Specifically focusing on frequency-based approach for text summarization Key Words: Automatic summarization, Extractive, Node package manager (npm), javascript, frequency-based.

Clinical research text summarization method based on fusion of domain knowledge

ObjectiveThe objective of this study is to integrate PICO knowledge into the clinical research text summarization process, aiming to enhance the model’s comprehension of biomedical texts while capturing crucial content from the perspective of summary readers, ultimately improving the quality of summaries. MethodsWe propose a clinical research text summarization method called DKGE-PEGASUS (Domain-Knowledge and Graph Convolutional Enhanced PEGASUS), which is based on integrating domain knowledge. The model mainly consists of three components: a PICO label prediction module, a text information re-mining unit based on Graph Convolutional Neural Networks (GCN), and a pre-trained summarization model. First, the PICO label prediction module is used to identify PICO elements in clinical research texts while obtaining word embeddings enriched with PICO knowledge. Then, we use GCN to reinforce the encoder of the pre-trained summarization model to achieve deeper text information mining while explicitly injecting PICO knowledge. Finally, the outputs of the PICO label prediction module, the GCN text information re-mining unit, and the encoder of the pre-trained model are fused to produce the final coding results, which are then decoded by the decoder to generate summaries. ResultsExperiments conducted on two datasets, PubMed and CDSR, demonstrated the effectiveness of our method. The Rouge-1 scores achieved were 42.64 and 38.57, respectively. Furthermore, the quality of our summarization results was found to significantly outperform the baseline model in comparisons of summarization results for a segment of biomedical text. ConclusionThe method proposed in this paper is better equipped to identify critical elements in clinical research texts and produce a higher-quality summary.

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>