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abstractive text summarization from Amazon food review dataset using modified attention based Bi-LSTM autoencoder model

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JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 2025年第3期48卷 268-282页

作者： Prabha, P. L. Parvathy, M. Sethu Inst Technol Dept Comp Sci & Engn Kariapatti 626115 India

Automatic text summarization (ATS) is extensively performed to obtain insights from long texts or documents through Natural Language Processing (NLP). Even though various DL and ML-based algorithms have been used to perform text summarization, there is a lack of extensive studies on abstractive text summarization. The attention mechanisms used in traditional text summarization methods generate a fixed length vector irrespective of input sequence length. Hence, this study enhances the summarization efficiency through the implementation of a modified attention mechanism to generate dynamic weights for the input sequence. The proposed Modified Attention-based Bi-directional LSTM with Auto Encoder (Matt BiLSTM-AE) model is implemented for abstractive text summarization. The Amazon food review dataset is loaded as an input dataset, which is pre-processed to remove stop words, punctuation, and special characters. The optimized weight adjustment is obtained through the dynamic attention mechanism in the proposed model. The performance of the proposed MAtt-Bi-LSTM-AE text summarization is evaluated through ROUGE, accuracy, and loss metrics. From performance analysis, the accuracy rate was found to be 74.6%, while the loss rate was found to be 1.1751.

关键词： abstractive text summarization dynamic attention natural language processing automatic text summarization autoencoder Bi-LSTM ROUGE

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abstractive text summarization using adversarial learning and deep neural network

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第17期83卷 50849-50870页

作者： Tank, Meenaxi Thakkar, Priyank Inst Technol Nirma Univ Dept Comp Sci Sarkhej Gandhinagar Highway Ahmadabad 382481 Gujarat India

A long-term objective of artificial intelligence is to design an abstractive text summarization (ATS) system that can produce condensed, adequate, and realistic summaries for the source documents. Deep learning approaches have contributed significantly to recent advancements in ATS, taking the state-of-the-art to new heights. Despite the considerable success of earlier approaches, producing high-quality and more human-like abstractive summaries continues to be a challenging task in reality. In this paper, we present an adversarial framework for abstractive text summarization, in which a generative model G and a proposed deep discriminative model D are trained in an adversarial manner. The goal of the generator is to generate summaries that are hard to differentiate from real summaries, whereas the discriminator's role is to estimate the probability that a summary came from the training data rather than the generator to direct the generative model's training. Experimental results on a benchmark CNN/Daily Mail dataset demonstrate that the proposed model achieves Rouge-1 and Rouge-L scores of 41.58 and 38.96 respectively which are better than the ones reported by various other methods (e.g. the base paper [1] achieved Rouge-1 and Rouge-L score of 39.92 and 36.71, while one of the recent works ACGT [2] achieved Rouge-1 score of 40.49 and Rouge-L score of 37.41). The manuscript's unique characteristic is qualitative evaluation, which, along with quantitative evaluation, shows that the proposed model is superior.

关键词： abstractive text summarization Deep neural networks Generative adversarial networks Adversarial learning Natural language processing

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abstractive text summarization for the Urdu Language: Data and Methods

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IEEE ACCESS 2024年 12卷 61198-61210页

作者： Awais, Muhammad Muhammad Adeel Nawab, Rao COMSATS Univ Islamabad Dept Comp Sci Lahore Campus Lahore 54000 Pakistan

The task of abstractive text summarization aims to automatically generate a short and concise summary of a given source article. In recent years, automatic abstractive text summarization has attracted the attention of researchers because large volumes of digital text are readily available in multiple languages on a wide range of topics. Automatically generating precise summaries from large text has potential application in the generation of news headlines, a summary of research articles, the moral of the stories, media marketing, search engine optimization, financial research, social media marketing, question-answering systems, and chatbots. In literature, the problem of abstractive text summarization has been mainly investigated for English and some other languages. However, it has not been thoroughly explored for the Urdu language despite having a huge amount of data available in digital format. To fulfill this gap, this paper presents a large benchmark corpus of 2,067,784 Urdu news articles for the Urdu abstractive text summarization task. As a secondary contribution, we applied a range of deep learning (LSTM, Bi-LSTM, LSTM with attention, GRU, Bi-GRU, and GRU with attention), and large language models (BART and GPT-3.5) on our proposed corpus. Our extensive evaluation on 20,000 test instances showed that GRU with attention model outperforms the other models with ROUGE- 1 = 46.7 , ROUGE- 2 = 24.1 , and ROUGE-L = 48.7. To foster research in Urdu, our proposed corpus is publically and freely available for research purposes under the Creative Common Licence.

关键词： Task analysis Long short term memory Deep learning Benchmark testing Social networking (online) Convolutional neural networks Natural language processing Abstracts text detection Artificial intelligence Publishing Unsupervised learning Machine learning text analysis text summarization abstractive text summarization BART corpus deep learning models GPT-3.5 large language models Urdu

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abstractive text summarization using LSTM-CNN based deep learning

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第1期78卷 857-875页

作者： Song, Shengli Huang, Haitao Ruan, Tongxiao Xidian Univ Software Engn Inst Xian 710071 Shaanxi Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China

The abstractive text summarization (ATS), which is the task of constructing summary sentences by merging facts from different source sentences and condensing them into a shorter representation while preserving information content and overall meaning. It is very difficult and time consuming for human beings to manually summarize large documents of text. In this paper, we propose an LSTM-CNN based ATS framework (ATSDL) that can construct new sentences by exploring more fine-grained fragments than sentences, namely, semantic phrases. Different from existing abstraction based approaches, ATSDL is composed of two main stages, the first of which extracts phrases from source sentences and the second generates text summaries using deep learning. Experimental results on the datasets CNN and DailyMail show that our ATSDL framework outperforms the state-of-the-art models in terms of both semantics and syntactic structure, and achieves competitive results on manual linguistic quality evaluation.

关键词： text mining abstractive text summarization Relation extraction Deep learning

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abstractive text summarization and new large-scale datasets for agglutinative languages Turkish and Hungarian

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LANGUAGE RESOURCES AND EVALUATION 2022年第3期56卷 973-1007页

作者： Baykara, Batuhan Gungor, Tunga Bogazici Univ Dept Comp Engn TR-34342 Istanbul Turkey

Due to the exponential growth in the number of documents on the Web, accessing the salient information relevant to a user need is gaining importance, which increases the popularity of text summarization. Recent progress in deep learning shifted the research in text summarization from extractive methods towards more abstractive approaches. The research and the available resources remain mostly limited to the English language, which prevents progress in other languages. There is need in low-resourced languages for gathering large-scale resources suitable for such tasks. In this study, we release two large-scale datasets (TR-News and HU-News) that can serve as benchmarks in the abstractive summarization task for Turkish and Hungarian. The datasets are primarily compiled for text summarization, but are also suitable for other tasks such as topic classification, title generation, and key phrase extraction. Morphology is important for these agglutinative languages since meaning is carried mostly within the morphemes of the words. We utilize these morphological properties for tokenization to retain the semantic information and reduce the vocabulary sparsity introduced by the agglutinative nature of these languages. Using the datasets compiled, we propose linguistically-oriented tokenization methods (SeperateSuffix and CombinedSuffix) and evaluate them on the state-of-the-art abstractive summarization models. The SeperateSuffix method achieves the highest ROUGE-1 score on the TR-News dataset and provides promising results on the HU-News dataset. In another experiment, we show that the multilingual cased BERT model outperforms monolingual BERT models for both languages and reaches the highest ROUGE-1 score on the HU-News dataset. Lastly, we provide qualitative analysis of the generated summaries on the TR-News dataset.

关键词： abstractive text summarization Morphological tokenization Agglutinative languages Turkish and Hungarian datasets

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abstractive text summarization Using Generative Adversarial Network and Relation Extraction

Abstractive Text Summarization Using Generative Adversarial ...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Jing, Liwei Yang, Lina Li, Xichun Meng, Zuqiang Guangxi Univ Sch Comp Elect & Informat Guangxi Peoples R China Guangxi Normal Univ Natl Chongzuo Peoples R China

ISBN: (纸本)9781665458412

In this paper, we propose a new adversarial model to solve some common problems existing in Generative Adversarial Network for text summarization. we simultaneously train a generative model G, a discriminative model D, and a model which auxiliary generator to generate a high quality summary .This module is like a teacher to guide G to quickly optimize himself and then against D, so we call it Teacher model. Teacher is an entity relationship extraction model to extract the triples of the real summary. The words in the triples are defined as keywords, and the keywords are revealed to the generator like the teacher points out the key points, and the summary generated by the guiding generator contains more Keywords. Model achieves competitive ROUGE scores with the baseline on CNN/Daily Mail dataset.

关键词： Entity and Relation Extraction Generative Adversarial Network abstractive text summarization

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abstractive text summarization Using Multimodal Information 10

Abstractive Text Summarization Using Multimodal Information

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10th International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Rafi, Shaik Das, Ranjita NIT MIZORAM Dept Comp Sci & Engn Aizawl 796012 Mizoram India NIT AGARTALA Dept Comp Sci & Engn Agartala 799046 Tripura India

ISBN: (纸本)9798350359374

Much text generates over the internet through news articles, story writing and blogs. Reading and understanding such an enormous amount of data to the user is problematic, including time and effort. Automatic abstractive text summarization has gained more importance to increase the user's understanding and reduce time. It shortens the given input by preserving the meaning and identifying the context of the whole document to generate meaningful sentences. The research community has proposed different methods for text reduction and generating abstractive summaries. However, problems like semantics and contextual relationship in the summary generation process must be still need to improve. The multimodal abstractive text summarization is a technique that combines text and image information which helps in addressing the semantics and contextual relationship by proposing Multimodality Image text (MIT) layer that fuses the text-extracted global features by glove embedding and preserves the semantics of the vocabulary and text-related images are used to identify the contextual relationship features from inception v3, which cope in the MIT layer to generate efficient multimodal abstractive text summaries by training and testing with seq-to-seq model. Experiments with the MSMO dataset achieve superior performance on other state-of-art results.

关键词： abstractive text summarization Multimodality Image text (MIT) Attention Mechanism LSTM Sequence-to-Sequence model

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abstractive text summarization with Hierarchical Multi-scale Abstraction Modeling and Dynamic Memory 21

Abstractive Text Summarization with Hierarchical Multi-scale...

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44th International ACM SIGIR Conference on Research and Development in Information Retrieval

作者： Wang, Lihan Yang, Min Li, Chengming Shen, Ying Xu, Ruifeng Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Harbin Inst Technol Shenzhen Shenzhen Peoples R China

ISBN: (纸本)9781450380379

In this paper, we propose a novel abstractive text summarization method with hierarchical multi-scale abstraction modeling and dynamic memory (called MADY). First, we propose a hierarchical multi-scale abstraction modeling method to capture the temporal dependencies of the document from multiple hierarchical levels of abstraction, which mimics the process of how human beings comprehend an article by learning fine timescales for low-level abstraction layers and coarse timescales for high-level abstraction layers. By applying this adaptive updating mechanism, the high-level abstraction layers are updated less frequently and expected to remember the long-term dependency better than the low-level abstraction layer. Second, we propose a dynamic key-value memory-augmented attention network to keep track of the attention history and comprehensive context information for the salient facets within the input document. In this way, our model can avoid generating repetitive words and faultiness summaries. Extensive experiments on two widely-used datasets demonstrate the effectiveness of the proposed MADY model in terms of both automatic evaluation and human evaluation. For reproducibility, we submit the code and data at: https://***/siat-nlp/***.

关键词： abstractive text summarization multi-scale abstraction modeling dynamic memory network

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abstractive text summarization on Google Search Results

Abstractive Text Summarization on Google Search Results

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International Conference on Intelligent Computing and Control Systems (ICICCS)

作者： Patel, Dikshita Shah, Nisarg Shah, Vrushali Hole, Varsha SPIT Dept Informat Technol Mumbai Maharashtra India

ISBN: (纸本)9781728148762

The Internet has become the forefront of our lives, technological advancements help people in an efficient and better way. The capitalists, educators, analysts all share a lot of content on the web so that it becomes available to users and helps them to connect with people. However, there is an issue in presenting the data to the users so that they can get the best information with the least effort. The search engine only helps to filter out irrelevant documents, leaving difficult tasks such as reading and summarizing to ourselves. Our paper proposes a model that will set an interface for web scraping using Python, The SSN techniques will prepare an abstractive summary from the data appearing in the search result links. This will allow people to access the best information with minimal efforts. The time-consuming and tedious tasks such as retrieving and summarizing are left to the computers. For all the impatient users, who don't have time to read the text carefully, such a system can be very useful to them.

关键词： abstractive text summarization SERP's LSTM GRU Seq2seq model

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abstractive text summarization Models Using Machine Learning Algorithms 7

Abstractive Text Summarization Models Using Machine Learning...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Narwadkar, Yogita P. Bagade, Anant M. SCTR's Pune Institute of Computer Technology Department of Computer Engineering Pune India SCTR's Pune Institute of Computer Technology Department of Information Technology Pune India

ISBN: (纸本)9798350304268

In today's era of cyberspace and intermedia, the number of e-documents have been enlarged enormously. The speedy growth of large data and documents in the field of Data Mining and emerging domain such as Information Retrieval (IR) and the demanding area of Natural Language Processing (NLP) needs Automated text summarization. text summarization is a process that decreases size of the source document while keeping its important information with actual meaning. It is burdensome and laborious for Homo sapiens to physically do the summarization of huge documents of text. Thus it is necessary to revise old methods and techniques of abstractive text summarization models and modernize them to be efficient and fulfill the requirements of users as per the need by resolving issues of continuously increased data. Now this paper presents a survey of the current futuristic models and the various algorithms and techniques used in abstractive text summarization systems liable to be subjected to an Encoder-Decoder framework. This research can help to gain the overall concepts of recent machine learning algorithms and encoder-decoder architecture resting on abstractive text summarization prototypes with a review of the challenges, issues, and future scope of these models. This research can help solve the research community's problems, newsletters, email overload, and media monitoring. © 2023 IEEE.

关键词： abstractive text summarization Evaluation Parameters Machine Learning encoder-decoder Natural Language Processing

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