Abstractive Text Summarization Research Articles

Bidirectional and Auto-Regressive Transformer (BART) for Indonesian Abstractive Text Summarization

Bidirectional and Auto-Regressive Transformer (BART) for Indonesian Abstractive Text Summarization

STAB: An Enhanced Abstractive Text Summarization Employing Stacked Bi-GRU with the Attention CNN Approach

STAB: An Enhanced Abstractive Text Summarization Employing Stacked Bi-GRU with the Attention CNN Approach

Abstractive text summarization: State of the art, challenges, and improvements

Specifically focusing on the landscape of abstractive text summarization, as opposed to extractive techniques, this survey presents a comprehensive overview, delving into state-of-the-art techniques, prevailing challenges, and prospective research directions. We categorize the techniques into traditional sequence-to-sequence models, pre-trained large language models, reinforcement learning, hierarchical methods, and multi-modal summarization. Unlike prior works that did not examine complexities, scalability and comparisons of techniques in detail, this review takes a comprehensive approach encompassing state-of-the-art methods, challenges, solutions, comparisons, limitations and charts out future improvements - providing researchers an extensive overview to advance abstractive summarization research. We provide vital comparison tables across techniques categorized - offering insights into model complexity, scalability and appropriate applications. The paper highlights challenges such as inadequate meaning representation, factual consistency, controllable text summarization, cross-lingual summarization, and evaluation metrics, among others. Solutions leveraging knowledge incorporation and other innovative strategies are proposed to address these challenges. The paper concludes by highlighting emerging research areas like factual inconsistency, domain-specific, cross-lingual, multilingual, and long-document summarization, as well as handling noisy data. Our objective is to provide researchers and practitioners with a structured overview of the domain, enabling them to better understand the current landscape and identify potential areas for further research and improvement.

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>

A survey on the dataset, techniques, and evaluation metric used for abstractive text summarization

A survey on the dataset, techniques, and evaluation metric used for abstractive text summarization

A Novel Deep Learning Attention Based Sequence to Sequence Model for Automatic Abstractive Text Summarization

A Novel Deep Learning Attention Based Sequence to Sequence Model for Automatic Abstractive Text Summarization

Sentence salience contrastive learning for abstractive text summarization

Abstractive Text summarization aims to generate a short summary for a document while preserving salient information. Recently, contrastive learning has been extended from visual representation to summarization tasks. At present, the methods of contrastive learning summarization focus on modeling the global semantics of source documents, targets and candidate summaries to maximize their similarities. However, they ignore the influence of sentence semantics in the document. In this paper, we propose a sentence-level salience contrastive learning method to help the model capture salient information and denoise. The model expresses the sentence salience according to the semantic similarity between the summaries and sentences of the source document, and integrates the similarity distance into the contrastive loss in the form of soft weights. Therefore, our model maximize the similarity between summaries and salient information, while minimizing the similarity between summaries and potential noise. We have verified our method in three widely used datasets, CNN/Daily Mail, XSum and PubMed. The experimental results show that the proposed method can significantly improve the baseline performance and achieve competitive performance in the existing contrastive learning methods.

IWM-LSTM encoder for abstractive text summarization

IWM-LSTM encoder for abstractive text summarization

Hindi Abstractive Text Summarization using Transliteration with Pre-trained Model

Automatic text summarization is a subarea of natural language processing that generates a summary of the text by keeping its key points. The research work done on summarizing low-resourced language text is very limited. In India, the Hindi language is being spoken by central and north Indian people and only a few research works have been done on abstractive summarization of Hindi language. Having matras in Hindi makes it difficult to tokenize so it is difficult to summarize Hindi text using abstractive text summarization. In the proposed method, abstractive Hindi text summarization is done using transliteration and fine-tuning. In this work, the model is trained to generate both summaries and headlines. ROUGE-score and BERT-score have been utilized to check summary quality. A new semantic similarity score-based performance measure is also proposed to measure semantic similarity between reference summaries and predicted summaries. Using the proposed method, we have achieved the highest 55.16 ROUGE score, 0.80 BERT score, and 0.98 similarity score. Along with these performance measures, human evaluation of predicted summaries is also done and it is found that summaries and headlines were generated at a human-acceptable level.

Improving Faithfulness in Abstractive Text Summarization with EDUs Using BART (Student Abstract)

Abstractive text summarization uses the summarizer’s own words to capture the main information of a source document in a summary. While it is more challenging to automate than extractive text summarization, recent advancements in deep learning approaches and pre-trained language models have improved its performance. However, abstractive text summarization still has issues such as unfaithfulness. To address this problem, we propose a new approach that utilizes important Elementary Discourse Units (EDUs) to guide BART-based text summarization. Our approach showed the improvement in truthfulness and source document coverage in comparison to some previous studies.

An Abstractive Text Summarization using Decoder Attention with Pointer Network

In contemporary times, an abundance of unstructured data prevails across social media and the web. Text summarization, a process aimed at distilling relevant information concisely without altering its core meaning, has become crucial. Manual text summarization is resource-intensive, prompting the exploration of automated methods. While deep learning algorithms, particularly in abstractive text summarization, have gained popularity, further research is needed to understand their integration with semantic-based or structure-based approaches. This research leverages a dataset of 1,735 resumes sourced from Kaggle to propose a novel framework. The framework combines semantic data transformations and deep learning approaches to enhance abstractive text summarization. A key focus is addressing the challenge of handling unregistered words. The proposed solution, Decoder Attention with Pointer Network (DA-PN), is introduced. DA-PN incorporates a coverage mechanism to mitigate word repetition in generated text summaries, thereby improving the quality of summaries. The method aims to safeguard against the propagation of errors in generated text summaries. The performance of the proposed approach is evaluated using the Recall Oriented Understudy for Gisting Evaluation (ROUGE) indicator. Notably, the proposed method achieves an average ROUGE score of 26.28, surpassing existing methods. The emphasis on combining semantic data transformations, deep learning, and addressing specific challenges like word repetition sets this research apart in the field of abstractive text summarization.

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.

Attention-based Transformer for Assamese Abstractive Text Summarization

The difficulty of accurately summarising Assamese text content is a significant barrier in natural language processing (NLP). Manually summarising lengthy Assamese texts is time-consuming and labor-intensive. As a result, automatic text summarization has developed as a critical NLP study topic. In this study, we integrate the Transformer and Self-Attention approaches to develop an abstract text summarization model. This Transformer-based technique uses self-attention approaches to successfully manage co-reference concerns in Assamese text, enhancing overall system understanding. This proposed approach improves the efficiency of text summarization greatly. We exhaustively evaluated the model using the Assamese dataset (AD-50), which contains human-produced summaries, to assess its performance. When compared to current state-of-the-art baseline models, our model outperformed them. On the AD-50 dataset, for example, our suggested model obtained a low training loss of 0.0022 during 20 training epochs, as well as an amazing model accuracy of 47.15 percentage. This research marks a substantial advancement in the field of Assamese abstractive text summarization, with intriguing implications for practical applications in NLP.

End to End Urdu Abstractive Text Summarization With Dataset and Improvement in Evaluation Metric

End to End Urdu Abstractive Text Summarization With Dataset and Improvement in Evaluation Metric

Exploring Abstractive Text Summarization: Methods, Dataset, Evaluation, and Emerging Challenges

Exploring Abstractive Text Summarization: Methods, Dataset, Evaluation, and Emerging Challenges

Abstractive text summarization using adversarial learning and deep neural network

Abstractive text summarization using adversarial learning and deep neural network

A hierarchical framework based on transformer technology to achieve factual consistent and non-redundant abstractive text summarization

A hierarchical framework based on transformer technology to achieve factual consistent and non-redundant abstractive text summarization

Constraint-Based Adversarial Networks for Unsupervised Abstract Text Summarization

Abstract text summarization is a classic sequence-to-sequence natural language generation task. In order to improve the quality of unsupervised abstract text summarization in unsupervised mode, we propose two constraints for training text summarization model, embedding space constraint and information ratio constraint. We construct a generative adversarial network with two discriminators based on these two constraints (TC-SUM-GAN). We use unsupervised and supervised methods to train the model in the experiment. Experimental results show that the ROUGE-1 value of the unsupervised TC-SUM-GAN increases by [Formula: see text] points compared with the basic model and at least 1.96 points compared with other comparative models. The ROUGE scores of the supervised TC-SUM-GAN are also improved. TC-SUM-GAN achieves very competitive results for the metrics of ROUGE-1 and ROUGE-2. In addition, the abstracts generated by our model are closer to those generated manually.

A machine translation model for abstractive text summarization based on natural language processing

“Knowledge is power and knowledge is liberating” conveys that there is a need for the capacity for creativity and that information is plentiful. The key application of natural language processing (NLP) is text summarization. It is a well-known technique for copying text, selecting accurate content, and get insight from the text. The purpose of this study is to propose for providing a summary of the text employing the seq2seq concept from the TensorFlow Python library. Through the use of deep learning-based data augmentation, the suggested method has the potential to increase the effectiveness of the text summary. Finally, the bilingual evaluation understudy (BLEU) criterion is used to judge the effectiveness of the suggested methodology

Improving colloquial case legal judgment prediction via abstractive text summarization

Most studies on Legal Judgment Prediction (LJP) use court verdicts or indictments as the training data source. Such models could assist judicial professionals who can use legal jargon to efficiently predict sentences. However, for ordinary and non-professional users, who can only provide a vague and incomplete description of the situation due to the lack of legal background, the predictive ability of the model will be greatly limited. To address this issue, we propose a colloquial case-based LJP framework called PekoNet, which incorporates Abstractive Text Summarization (ATS) into training for the LJP task to improve prediction accuracy for colloquial case description. We considered two approaches: independent training and joint training. The former train two separate model independently, while the latter jointly train both the ATS and LJP modules with either ATS-Freezing or ATS-Finetuning. The performance of these models is evaluated on two automated summarized testing datasets - BART and ChatGPT as well as human-provided case summaries. The results of the experiments demonstrate that the models developed with PekoNet outperform the typical LJP model for colloquial case description by up to 3.6%-10.8%.