Text Summarization Research Articles

Adapted large language models can outperform medical experts in clinical text summarization.

Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown promise in natural language processing (NLP) tasks, their effectiveness on a diverse range of clinical summarization tasks remains unproven. Here we applied adaptation methods to eight LLMs, spanning four distinct clinical summarization tasks: radiology reports, patient questions, progress notes and doctor-patient dialogue. Quantitative assessments with syntactic, semantic and conceptual NLP metrics reveal trade-offs between models and adaptation methods. A clinical reader study with 10 physicians evaluated summary completeness, correctness and conciseness; in most cases, summaries from our best-adapted LLMs were deemed either equivalent (45%) or superior (36%) compared with summaries from medical experts. The ensuing safety analysis highlights challenges faced by both LLMs and medical experts, as we connect errors to potential medical harm and categorize types of fabricated information. Our research provides evidence of LLMs outperforming medical experts in clinical text summarization across multiple tasks. This suggests that integrating LLMs into clinical workflows could alleviate documentation burden, allowing clinicians to focus more on patient care.

Text Summarization for Online and Blended Learning

Online learning text summarization is vital for managing the constant influx of online information. It involves condensing lengthy online content into concise summaries while retaining the original meaning and information. While several online summarization tools are available, they often fall short in preserving the underlying semantics of the text. In this paper, we introduce an innovative approach to online text summarization that strongly emphasizes capturing and preserving the semantics of the text. Our automatic summarizer leverages distributional semantic models to extract and incorporate semantics, producing high-quality online summaries. To evaluate the effectiveness of our online summarization system, we conducted experiments on a diverse range of online content. We employed ROUGE metrics, a popular evaluation method for text summarization, to assess our system's performance. Additionally, we compared our results with those of four state-of-the-art online summarizers. The outcome of our study demonstrates that our online summarization approach, which integrates semantics as a fundamental feature, outperforms other reference summarizers. This conclusion underscores the significance of leveraging semantics in the context of online learning text summarization. Furthermore, our system's ability to reduce redundancies in online content makes it a valuable tool for managing information overload in the digital age.

Two-Stream Network for Korean Natural Language Understanding

This study pioneers a dual-stream network architecture tailored for Korean Natural Language Understanding (NLU), focusing on enhancing comprehension by distinct processing of syntactic and semantic aspects. The hypothesis is that this bifurcation can lead to a more nuanced and accurate understanding of the Korean language, which often presents unique syntactic and semantic challenges not fully addressed by generalized models. The validation of this novel architecture employs the Korean Natural Language Inference (koNLI) and Korean Semantic Textual Similarity (koSTS) datasets. By evaluating the model's performance on these datasets, the study aims to determine its efficacy in accurately parsing and interpreting Korean text's syntactic structure and semantic meaning. Preliminary results from this research are promising. They indicate that the dual-stream approach significantly enhances the model's capability to understand and interpret complex Korean sentences. This improvement is crucial in NLU, especially for language-specific applications. The implications of this study are far-reaching. The methodology and findings could pave the way for more sophisticated NLU applications tailored to the Korean language, such as advanced sentiment analysis, nuanced text summarization, and more effective conversational AI systems. Moreover, this research contributes significantly to the broader field of NLU by underscoring the importance and efficacy of developing language-specific models, moving beyond the one-size-fits-all approach of general language models. Thus, this study is a testament to the potential of specialized approaches in language understanding technologies.

A Novel Approach to Text Summarization Using Machine Learning

Text summarization is a key strategy in the domains of information retrieval and natural language processing (NLP). Its objective is to reduce a lengthy written document into a clearer, more succinct summary of the information it contains. When a text document is too lengthy or intricate to analyse completely, as in news stories, academic papers, or legal documents, this approach is extremely helpful. The major challenge of text summarising is to take the most important and relevant information from the original text and convey it in an understandable and concise way. In this study, extractive and abstractive summarising techniques are the two primary categories of text summary methods. The paper also presents several algorithms that have been proposed for text summarization, including TextRank, Seq2Seq, and BART. TextRank is a simple and fast algorithm that works well for short documents, Seq2Seq is a deep learning-based approach that generates high-quality summaries, and BART is a transformer-based algorithm that provides the best results on benchmark datasets. The obtained ROUGE Score after passing TextRank, BART, and Seq2SEq algorithm significant also.

A Multi-Metric Model for analyzing and comparing extractive text summarization approaches and algorithms on scientific papers

In today's world, where data and information are increasingly proliferating, text summarization and technologies play a critical role in making large amounts of text data more accessible and meaningful. In business, the news industry, academic research, and many other fields, text summarization helps make quick decisions, access information faster, and manage resources more effectively. Additionally, text summarization research is conducted to further improve these technologies and develop new methods and algorithms to provide better summarization of texts. Therefore, text summarization and research in this field are of great importance in the information age. In this study, a new operating model for text summarization that can be applied to different algorithms is proposed and evaluated. Sixteen summarization algorithms covering six approaches (statistical, graph-based, content-based, pointer-based, position-based, and user-oriented) were implemented and tested on 50 different full-text article datasets. Four evaluation criteria (BLEU, Rouge-N, Rouge-L, METEOR) were used to assess the similarity between the generated summaries and the original summaries. The performance of the algorithms within each approach was averaged and the overall best-performing algorithm was selected. This best algorithm was subjected to further analysis through Topic Modelling and Keyword Extraction to identify key topics and keywords within the summarised text. The proposed model provides a standardized workflow for developing and thoroughly testing summarization algorithms across datasets and evaluation metrics to determine the most appropriate summarization approach. This study demonstrates the effectiveness of the model on a variety of algorithm types and text sources.

Is industrial policy back in fashion? Text-as-data evidence from UK policy documents

Abstract This article evaluates the claim that industrial policy is seeing a revival in developed economies, using text-as-data evidence from UK government policy papers. Structural topic modeling shows that content which can be related to industrial policy has indeed seen a large increase in prevalence over the past decade compared to the baseline of the post-1980 liberal era. Moreover, such content is shown to be increasingly central to post-2010 economic policy based on its position in the network of topics, on the number of downloads of documents associated with it, and on inclusion in important papers. An automated text summarization algorithm is used to extract the fragments which are most representative for these developments, and these are shown to closely match common definitions of industrial policy. A sentiment analysis algorithm is then used to extract the motivations given for policy proposals in representative documents, and indicates that declining economic competitiveness is a central concern.

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.

A Review- Machine Learning Techniques for Text Summarization

Aspect ranking framework is significant to identify the important aspects from numerous consumer reviews posted in various domains like hotel, movie and product etc. They could be broadly classified into supervised and unsupervised approaches broadly. Supervised methods rely on semantic knowledge bases. These are found to be effective for ranking compared to conventional approaches. These methods available in the literature are discussed in detail. Next, this review focuses on the extractive summarization systems, in which the summary is generated by picking a sub-set of sentences from the related text. Extractive summarization systems that utilize machine learning, optimization and map reduce framework are explained elaborately. This is due to the efficiency of these techniques reported in the comprehensive works available for text summarization. A literature covering text similarity discovery methods employing text, semantic information and graph based systems are presented in detail at the end of the chapter. Among these graph based methods play a vital role in current field of the research

Survey of transformers and towards ensemble learning using transformers for natural language processing

The transformer model is a famous natural language processing model proposed by Google in 2017. Now, with the extensive development of deep learning, many natural language processing tasks can be solved by deep learning methods. After the BERT model was proposed, many pre-trained models such as the XLNet model, the RoBERTa model, and the ALBERT model were also proposed in the research community. These models perform very well in various natural language processing tasks. In this paper, we describe and compare these well-known models. In addition, we also apply several types of existing and well-known models which are the BERT model, the XLNet model, the RoBERTa model, the GPT2 model, and the ALBERT model to different existing and well-known natural language processing tasks, and analyze each model based on their performance. There are a few papers that comprehensively compare various transformer models. In our paper, we use six types of well-known tasks, such as sentiment analysis, question answering, text generation, text summarization, name entity recognition, and topic modeling tasks to compare the performance of various transformer models. In addition, using the existing models, we also propose ensemble learning models for the different natural language processing tasks. The results show that our ensemble learning models perform better than a single classifier on specific tasks.Graphical

Abstractive text summarization model combining a hierarchical attention mechanism and multiobjective reinforcement learning

Text summarization research is significant and challenging in the domain of natural language processing. Abstractive text summarization mainly uses the encoder-decoder framework, wherein the encoder component does not have a sufficient semantic comprehension of the input text, and there are exposure biases and semantic inconsistencies between the reference and generated summaries during the training process. We propose an improved encoder-decoder model that incorporates a hierarchical attention mechanism and multiobjective reinforcement learning. The encoder introduces a multihead self-attention mechanism to allow for the acquisition of more comprehensive semantic information from multiple angles and dimensions, while the decoder introduces a pointer-generator network to solve the out-of-vocabulary problem. Multiobjective reinforcement learning methods are constructed throughout the training process to optimize the model in terms of addressing exposure bias, maintaining semantic consistency, and enhancing readability. The results of the comparative experiments demonstrate that the proposed model significantly improved in terms of the ROUGE evaluation metric, and the generated summaries were semantically similar to the reference summaries.

ChatGPT: Challenges and Benefits in Software Programming for Higher Education

ChatGPT is a substantial language model developed by OpenAI, rooted in the GPT-3.5 architecture, with the capacity to generate human-like responses to text-based inputs. ChatGPT serves various purposes, encompassing chatbots, customer service, and personal assistants, which can significantly contribute to sustainability initiatives. Its applications range from language translation and content creation to text summarization. Utilizing ChatGPT offers several advantages, notably its rapid response generation, high accuracy, and its capacity to evolve and improve over time, aligning with sustainability goals for efficiency and innovation. In an educational context, ChatGPT can provide invaluable support to students and educators, aiding in tasks such as generating summaries for extensive texts and addressing subject-related queries. For programming education, ChatGPT can assist students with coding assignments by offering suggestions, hints, and even generating code snippets, fostering sustainable coding practices. Nevertheless, employing ChatGPT in coding education presents challenges, particularly the risk of students becoming overly dependent on AI-generated code and failing to grasp fundamental concepts, which can hinder long-term sustainability in the field. To gauge the viability of ChatGPT in programming education and sustainability, we conducted a Likert scale questionnaire with a group of 40 Brazilian students from March to April 2023. Our primary goal was to assess students’ interest in utilizing ChatGPT as a tool to face programming challenges and problems. Specifically, we aimed to determine their level of inclination towards relying exclusively on ChatGPT during programming classes. In addition to these objectives, we sought to discern not only the positive and beneficial perceptions of using ChatGPT in the classroom but also to investigate its potential impact on learning outcomes and student engagement. Furthermore, we aimed to explore whether participants would consider transitioning to exclusive reliance on ChatGPT in the context of their programming education. Our study revealed that students recognized ChatGPT as an innovative set of AI tools applicable to various classroom contexts, including programming and computer languages, thereby fostering sustainability in the adoption of AI technology for educational purposes. Notably, a majority of students participating in the study expressed a keen interest in employing this tool as a supplementary educational resource in the classroom, promoting sustainable and enhanced learning experiences.

Multimodal Abstractive Summarization using bidirectional encoder representations from transformers with attention mechanism

In recent decades, abstractive text summarization using multimodal input has attracted many researchers due to the capability of gathering information from various sources to create a concise summary. However, the existing methodologies based on multimodal summarization provide only a summary for the short videos and poor results for the lengthy videos. To address the aforementioned issues, this research presented the Multimodal Abstractive Summarization using Bidirectional Encoder Representations from Transformers (MAS-BERT) with an attention mechanism. The purpose of the video summarization is to increase the speed of searching for a large collection of videos so that the users can quickly decide whether the video is relevant or not by reading the summary. Initially, the data is obtained from the publicly available How2 dataset and is encoded using the Bidirectional Gated Recurrent Unit (Bi-GRU) encoder and the Long Short Term Memory (LSTM) encoder. The textual data which is embedded in the embedding layer is encoded using a bidirectional GRU encoder and the features with audio and video data are encoded with LSTM encoder. After this, BERT based attention mechanism is used to combine the modalities and finally, the BI-GRU based decoder is used for summarizing the multimodalities. The results obtained through the experiments that show the proposed MAS-BERT has achieved a better result of 60.2 for Rouge-1 whereas, the existing Decoder-only Multimodal Transformer (D-MmT) and the Factorized Multimodal Transformer based Decoder Only Language model (FLORAL) has achieved 49.58 and 56.89 respectively. Our work facilitates users by providing better contextual information and user experience and would help video-sharing platforms for customer retention by allowing users to search for relevant videos by looking at its summary.

Deep sequential pattern mining for readability enhancement of Indonesian summarization

In text summarization research, readability is a great issue that must be addressed. Our hypothesis is readability can be accomplished by using text representations that keep the meaning of text documents intact. Therefore, this study aims to combine sequential pattern mining (SPM) in producing a sequence of a word as text representation with unsupervised deep learning to produce an Indonesian text summary called DeepSPM. This research uses PrefixSpan as an SPM algorithm and deep belief network (DBN) as an unsupervised deep learning method. This research uses 18,774 Indonesian news text from IndoSum. The readability aspect is evaluated by recall-oriented understudy for gisting evaluation (ROUGE) as a co-selection-based analysis; Dwiyanto Djoko Pranowo metrics, Gunning fog index (GFI), and Flesch-Kincaid grade level (FKGL) as content-based analysis; and human readability evaluation with two experts. The experiment result shows that DeepSPM yields better than DBN, with the F-measure value of ROUGE-1 enhanced to 0.462, ROUGE-2 is 0.37, and ROUGE-L is 0.41. The significance of ROUGE results also be tested using T-Test. The content-based analysis and human readability evaluation findings are conformable with the findings of co-selection-based analysis that generated summaries are only partially readable or have a medium level of readability aspect.

Text Summarization Using NLP

Abstract: This Project represents the work associated with Text Summarization. In this paper, we present a framework for summarizing the massive facts. The proposed framework depends on highlight extraction from internet, using each morphological element and semantic data. Presently, in which large facts is available on the internet, it's far maximum important to offer the improved approaches to extract the statistics quickly and most successfully. It may be very hard for human beings to manually extract the precis of a large document of text. There are lots of text substances to be had on the internet. So, there's a hassle of looking for related files from the quantity of documents to be had and absorbing associated statistics from it. In essence to determine out the previous issues, the automated textual content summarization could be very a whole lot essential. Text Summarization is the method of figuring out the most vital and significant information in a input report or set of related input files and compressing all the inputs into a shorter version preserving its normal goals.

UNIFIED COMMUNICATION: A SURVEY ON HARMONIZING REGIONAL LANGUAGE DIVERSITY

This project tackles the complex task of extracting insights from text and images using Optical Character Recognition (OCR). After extracting text, language identification is crucial for a comprehensive multiclass classification approach, especially given the limitations of existing machine translation systems for Indian languages. The paper carefully examines challenges in machine translation, morphological analysis, parsing, word sense disambiguation, and the translation process to enhance the quality of translations. Beyond translation, the project includes automatic text summarization to distill essential content. Through the seamless integration of OCR, language detection, translation, and text summarization, our approach aims to facilitate unified communication by harmonizing diverse voices in multilingual settings

Benchmarking Large Language Models for News Summarization

Abstract Large language models (LLMs) have shown promise for automatic summarization but the reasons behind their successes are poorly understood. By conducting a human evaluation on ten LLMs across different pretraining methods, prompts, and model scales, we make two important observations. First, we find instruction tuning, not model size, is the key to the LLM’s zero-shot summarization capability. Second, existing studies have been limited by low-quality references, leading to underestimates of human performance and lower few-shot and finetuning performance. To better evaluate LLMs, we perform human evaluation over high-quality summaries we collect from freelance writers. Despite major stylistic differences such as the amount of paraphrasing, we find that LLM summaries are judged to be on par with human written summaries.

An Improved mT5 Model for Chinese Text Summary Generation

Complicated policy texts require a lot of effort to read, so there is a need for intelligent interpretation of Chinese policies. To better solve the Chinese Text Summarization task, this paper utilized the mT5 model as the core framework and initial weights. Additionally, In addition, this paper reduced the model size through parameter clipping, used the Gap Sentence Generation (GSG) method as unsupervised method, and improved the Chinese tokenizer. After training on a meticulously processed 30GB Chinese training corpus, the paper developed the enhanced mT5-GSG model. Then, when fine-tuning the Chinese Policy text, this paper chose the idea of “Dropout Twice”, and innovatively combined the probability distribution of the two Dropouts through the Wasserstein distance. Experimental results indicate that the proposed model achieved Rouge-1, Rouge-2, and Rouge-L scores of 56.13%, 45.76%, and 56.41% respectively on the Chinese policy text summarization dataset.

Automatic Text Summarization Method Based on Improved TextRank Algorithm and K-Means Clustering

Automatic text summarization is to obtain a summary by compressing the text while retaining its important information. Then users can obtain the important content of the text by reading the summary. In the research literatures, the extraction summary method is widely used and is also one type of the main research methods of summary methods. However, this extraction summary method still has some problems. The selection of the initial cluster center has not been carefully determined, and the sentence redundancy summarized is high in articles with complex sentences. In order to solve the above problems, this paper proposes an automatic text summarization method based on improved TextRank algorithm and K-Means clustering. This method combines the improved BM25 model and the TextRank algorithm to calculate the BM25 similarity between sentences and obtain the TR scores of sentences. The TR scores are used to select the initial center of clustering based on similarity difference judgment and maximum judgment. The final summary is obtained by combining the cluster scores and sentence scores. The experimental results show that the proposed method in this paper has better evaluation indicators containing ROUGE-1, ROUGE-2 and ROUGE-L than other comparison algorithms including Lead-3, TextRank and MBM25EMB on the DUC2004 dataset. In conclusion, the proposed method in this paper improves the accuracy of automatic text summarization and reduce the redundancy from documents.

DNN-HHOA: Deep Neural Network Optimization-Based Tabular Data Extraction from Compound Document Images

Text information extraction from a tabular structure within a compound document image (CDI) is crucial to help better understand the document. The main objective of text extraction is to extract only helpful information since tabular data represents the relation between text lying in a tuple. Text from an image may be of low contrast, different style, size, alignment, orientation, and complex background. This work presents a three-step tabular text extraction process, including pre-processing, separation, and extraction. The pre-processing step uses the guide image filter to remove various kinds of noise from the image. Improved binomial thresholding (IBT) separates the text from the image. Then the tabular text is recognized and extracted from CDI using deep neural network (DNN). In this work, weights of DNN layers are optimized by the Harris Hawk optimization algorithm (HHOA). Obtained text and associated information can be used in many ways, including replicating the document in digital format, information retrieval, and text summarization. The proposed process is applied comprehensively to UNLV, TableBank, and ICDAR 2013 image datasets. The complete procedure is implemented in Python, and precision metrics performance is verified.

Exploring the Cognitive Neural Basis of Factuality in Abstractive Text Summarization Models: Interpretable Insights from EEG Signals

(1) Background: Information overload challenges decision-making in the Industry 4.0 era. While Natural Language Processing (NLP), especially Automatic Text Summarization (ATS), offers solutions, issues with factual accuracy persist. This research bridges cognitive neuroscience and NLP, aiming to improve model interpretability. (2) Methods: This research examined four fact extraction techniques: dependency relation, named entity recognition, part-of-speech tagging, and TF-IDF, in order to explore their correlation with human EEG signals. Representational Similarity Analysis (RSA) was applied to gauge the relationship between language models and brain activity. (3) Results: Named entity recognition showed the highest sensitivity to EEG signals, marking the most significant differentiation between factual and non-factual words with a score of −0.99. The dependency relation followed with −0.90, while part-of-speech tagging and TF-IDF resulted in 0.07 and −0.52, respectively. Deep language models such as GloVe, BERT, and GPT-2 exhibited noticeable influences on RSA scores, highlighting the nuanced interplay between brain activity and these models. (4) Conclusions: Our findings emphasize the crucial role of named entity recognition and dependency relations in fact extraction and demonstrate the independent effects of different models and TOIs on RSA scores. These insights aim to refine algorithms to reflect human text processing better, thereby enhancing ATS models’ factual integrity.