15 results on '"TEXT summarization"'
Search Results
2. Natural Language Processing Challenges and Issues: A Literature Review.
- Author
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ABRO, Abdul Ahad, Hussain TALPUR, Mir Sajjad, and JUMANI, Awais Khan
- Subjects
- *
LITERATURE reviews , *TEXT summarization , *NATURAL language processing , *MACHINE translating , *ARTIFICIAL intelligence , *MACHINE learning - Abstract
Natural Language Processing (NLP) is the computerized approach to analyzing text using both structured and unstructured data. NLP is a simple, empirically powerful, and reliable approach. It achieves state-of-the-art performance in language processing tasks like Semantic Search (SS), Machine Translation (MT), Text Summarization (TS), Sentiment Analyzer (SA), Named Entity Recognition (NER) and Emotion Detection (ED). NLP is expected to be the technology of the future, based on current technology deployment and adoption. The primary question is: What does NLP have to offer in terms of reality, and what are the prospects? There are several problems to be addressed with this developing method, as it must be compatible with future technology. In this paper, the benefits, challenges and limitations of this innovative paradigm along with the areas open to do research are shown. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
3. Attribute-Sentiment-Guided Summarization of User Opinions From Online Reviews.
- Author
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Yi Han, Nanda, Gaurav, and Moghaddam, Mohsen
- Subjects
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TEXT summarization , *LANGUAGE models , *CONSUMERS' reviews , *NATURAL language processing , *PRODUCT attributes - Abstract
Eliciting informative user opinions from online reviews is a key success factor for innovative product design and development. The unstructured, noisy, and verbose nature of user reviews, however, often complicate large-scale need finding in a format useful for designers without losing important information. Recent advances in abstractive text summarization have created the opportunity to systematically generate opinion summaries from online reviews to inform the early stages of product design and development. However, two knowledge gaps hinder the applicability of opinion summarization methods in practice. First, there is a lack of formal mechanisms to guide the generative process with respect to different categories of product attributes and user sentiments. Second, the annotated training datasets needed for supervised training of abstractive summarization models are often difficult and costly to create. This article addresses these gaps by (1) devising an efficient computational framework for abstractive opinion summarization guided by specific product attributes and sentiment polarities, and (2) automatically generating a synthetic training dataset that captures various degrees of granularity and polarity. A hierarchical multi-instance attribute-sentiment inference model is developed for assembling a high-quality synthetic dataset, which is utilized to fine-tune a pretrained language model for abstractive summary generation. Numerical experiments conducted on a large dataset scraped from three major e-Commerce retail stores for apparel and footwear products indicate the performance, feasibility, and potentials of the developed framework. Several directions are provided for future exploration in the area of automated opinion summarization for user-centered design. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
4. Cryptoblend: An AI-Powered Tool for Aggregation and Summarization of Cryptocurrency News.
- Author
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Pozzi, Andrea, Barbierato, Enrico, and Toti, Daniele
- Subjects
ARTIFICIAL intelligence ,NATURAL language processing ,MACHINE learning ,CRYPTOCURRENCIES ,ELECTRONIC newspapers ,TEXT summarization ,WEB-based user interfaces - Abstract
In the last decade, the techniques of news aggregation and summarization have been increasingly gaining relevance for providing users on the web with condensed and unbiased information. Indeed, the recent development of successful machine learning algorithms, such as those based on the transformers architecture, have made it possible to create effective tools for capturing and elaborating news from the Internet. In this regard, this work proposes, for the first time in the literature to the best of the authors' knowledge, a methodology for the application of such techniques in news related to cryptocurrencies and the blockchain, whose quick reading can be deemed as extremely useful to operators in the financial sector. Specifically, cutting-edge solutions in the field of natural language processing were employed to cluster news by topic and summarize the corresponding articles published by different newspapers. The results achieved on 22,282 news articles show the effectiveness of the proposed methodology in most of the cases, with 86.8 % of the examined summaries being considered as coherent and 95.7 % of the corresponding articles correctly aggregated. This methodology was implemented in a freely accessible web application. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
5. THIS PC DOES NOT EXIST.
- Author
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Evenden, Ian
- Subjects
MACHINE learning ,NATURAL language processing ,LANGUAGE models ,ARTIFICIAL intelligence ,CHATBOTS ,INTELLIGENT personal assistants ,TEXT summarization - Abstract
Nobody at the time called Clippy an AI, but at heart what we're seeing in the AIs of today is a development of the same thing. The current crop of AIs are essentially algorithms, dependent on their training data for the responses they give to prompts. BUILD A GAMING POWERHOUSE The question of artificial intelligence has been a hard one to get away from, with ChatGPT hitting the headlines as both an amazing scientific advance and the harbinger of the end of civilization. [Extracted from the article]
- Published
- 2023
6. Natural Language Generation: Improving the Accessibility of Causal Modeling Through Applied Deep Learning
- Author
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Gandee, Tyler John
- Subjects
- Computer Science, Artificial Intelligence, AI, Applied Machine Learning, Causal Modeling, Generative Artificial Intelligence, Generative AI, Graph Reduction, Machine Learning, Natural Language Generation, Participatory Modeling, Text Generation, Text Summarization, Visual Analytics
- Abstract
Causal maps are graphical models that are well-understood in small scales. When created through a participatory modeling process, they become a strong asset in decision making. Furthermore, those who participate in the modeling process may seek to understand the problem from various perspectives. However, as causal maps increase in size, the information they contain becomes clouded, which results in the map being unusable. In this thesis, we transform causal maps into various mediums to improve the usability and accessibility of large causal models; our proposed algorithms can also be applied to small-scale causal maps. In particular, we transform causal maps into meaningful paragraphs using GPT and network traversal algorithms to attain full-coverage of the map. Then, we compare automatic text summarization models with graph reduction algorithms to reduce the amount of text to a more approachable size. Finally, we combine our algorithms into a visual analytics environment to provide details-on-demand for the user by displaying the summarized text, and interacting with summaries to display the detailed text, causal map, and even generate images in an appropriate manner. We hope this research provides more tools for decision-makers and allows modelers to give back to participants the final result of their work.
- Published
- 2024
7. From Human Grading to Machine Grading: Automatic Diagnosis of e-Book Text Marking Skills in Precision Education.
- Author
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Yang, Albert C. M., Chen, Irene Y. L., Flanagan, Brendan, and Hiroaki Ogata
- Subjects
- *
ABILITY , *DIGITAL learning , *ARTIFICIAL intelligence , *MACHINE learning , *INTELLIGENT tutoring systems , *TIME management - Abstract
Precision education is a new challenge in leveraging artificial intelligence, machine learning, and learning analytics to enhance teaching quality and learning performance. To facilitate precision education, text marking skills can be used to determine students' learning process. Text marking is an essential learning skill in reading. In this study, we proposed a model that leverages the state-of-the-art text summarization technique, Bidirectional Encoder Representations from Transformers (BERT), to calculate the marking score for 130 graduate students enrolled in an accounting course. Then, we applied learning analytics to analyze the correlation between their marking scores and learning performance. We measured students' self-regulated learning (SRL) and clustered them into four groups based on their marking scores and marking frequencies to examine whether differences in reading skills and text marking influence students' learning performance and awareness of self-regulation. Consistent with past research, our results did not indicate a strong relationship between marking scores and learning performance. However, high-skill readers who use more marking strategies perform better in learning performance, task strategies, and time management than high-skill readers who use fewer marking strategies. Furthermore, high-skill readers who actively employ marking strategies also achieve superior scores of environment structure, and task strategies in SRL than low-skill readers who are inactive in marking. The findings of this research provide evidence supporting the importance of monitoring and training students' text marking skill and facilitating precision education. [ABSTRACT FROM AUTHOR]
- Published
- 2021
8. A Supervised Approach to Arabic Text Summarization Using AdaBoost
- Author
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Belkebir, Riadh, Guessoum, Ahmed, Kacprzyk, Janusz, Series editor, Rocha, Alvaro, editor, Correia, Ana Maria, editor, Costanzo, Sandra, editor, and Reis, Luis Paulo, editor
- Published
- 2015
- Full Text
- View/download PDF
9. Cryptoblend: An AI-Powered Tool for Aggregation and Summarization of Cryptocurrency News
- Author
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Andrea Pozzi, Enrico Barbierato, and Daniele Toti
- Subjects
Human-Computer Interaction ,blockchain ,machine learning ,web development ,Computer Networks and Communications ,Communication ,natural language processing ,hierarchical clustering ,text summarization ,noSQL database ,artificial intelligence ,Settore ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI - Abstract
In the last decade, the techniques of news aggregation and summarization have been increasingly gaining relevance for providing users on the web with condensed and unbiased information. Indeed, the recent development of successful machine learning algorithms, such as those based on the transformers architecture, have made it possible to create effective tools for capturing and elaborating news from the Internet. In this regard, this work proposes, for the first time in the literature to the best of the authors’ knowledge, a methodology for the application of such techniques in news related to cryptocurrencies and the blockchain, whose quick reading can be deemed as extremely useful to operators in the financial sector. Specifically, cutting-edge solutions in the field of natural language processing were employed to cluster news by topic and summarize the corresponding articles published by different newspapers. The results achieved on 22,282 news articles show the effectiveness of the proposed methodology in most of the cases, with 86.8% of the examined summaries being considered as coherent and 95.7% of the corresponding articles correctly aggregated. This methodology was implemented in a freely accessible web application.
- Published
- 2023
- Full Text
- View/download PDF
10. Learning-Free Unsupervised Extractive Summarization Model
- Author
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Pilsung Kang and Myeongjun Jang
- Subjects
General Computer Science ,Computer science ,Process (engineering) ,sentence representation vector ,Feature extraction ,02 engineering and technology ,010501 environmental sciences ,Machine learning ,computer.software_genre ,01 natural sciences ,integer linear programming ,0202 electrical engineering, electronic engineering, information engineering ,General Materials Science ,natural language processing ,0105 earth and related environmental sciences ,Artificial neural network ,business.industry ,Deep learning ,General Engineering ,Automatic summarization ,Text summarization ,Task analysis ,Embedding ,020201 artificial intelligence & image processing ,Artificial intelligence ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,business ,computer ,lcsh:TK1-9971 ,Sentence - Abstract
Text summarization is an information condensation technique that abbreviates a source document to a few representative sentences with the intention to create a coherent summary containing relevant information of source corpora. This promising subject has been rapidly developed since the advent of deep learning. However, summarization models based on deep neural network have several critical shortcomings. First, a large amount of labeled training data is necessary. This problem is standard for low-resource languages in which publicly available labeled data do not exist. In addition, a significant amount of computational ability is required to train neural models with enormous network parameters. In this study, we propose a model called Learning Free Integer Programming Summarizer (LFIP-SUM), which is an unsupervised extractive summarization model. The advantage of our approach is that parameter training is unnecessary because the model does not require any labeled training data. To achieve this, we formulate an integer programming problem based on pre-trained sentence embedding vectors. We also use principal component analysis to automatically determine the number of sentences to be extracted and to evaluate the importance of each sentence. Experimental results demonstrate that the proposed model exhibits generally acceptable performance compared with deep learning summarization models although it does not learn any parameters during the model construction process.
- Published
- 2021
11. Argument Extraction for Key Point Generation Using MMR-Based Methods
- Author
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Kenji Araki, Daiki Shirafuji, and Rafal Rzepka
- Subjects
key points ,General Computer Science ,Computer science ,text summarization ,computer.software_genre ,Task (project management) ,Licenses ,Tools ,Argument ,Portals ,Similarity (psychology) ,General Materials Science ,Relevance (information retrieval) ,natural language processing ,Electrical and Electronic Engineering ,Set (psychology) ,business.industry ,argument mining ,General Engineering ,Computational modeling ,Automatic summarization ,Correlation ,TK1-9971 ,machine learning ,Bit error rate ,Argument aggregation ,Task analysis ,Key (cryptography) ,Electrical engineering. Electronics. Nuclear engineering ,Artificial intelligence ,business ,computer ,Natural language processing - Abstract
When people debate, they want to familiarize themselves with a whole range of arguments about a given topic in order to deepen their knowledge and inspire new claims. However, the amount of differently phrased arguments is humongous, making the process of processing them time-consuming. In spite of many works on using arguments (e.g. counter-argument generation), there is only a few studies on argument aggregation. To address this problem, we propose a new task in argument mining – Argument Extraction, which gathers similar arguments into key points, usually single sentences describing a set of arguments for a given debate topic. Such a short summary of related arguments has been manually created in previous research, while in our research key point generation becomes fully automatic, saving time and cost. As the first step of key point generation we explore existing similarity calculation methods, i.e. Sentence-BERT and MoverScore to investigate their performance. Next, we propose a combination of argument similarity and Maximal Marginal Relevance (MMR) for extracting key phrases to be utilized in our novel task of Argument Extraction. Experimental results show that MoverScore-based MMR outperforms strong baselines covering 72.5% of arguments when eleven or more arguments are extracted. This percentage is almost identical with the cover rate of human-made key points.
- Published
- 2021
12. An Attention-Based Syntax-Tree and Tree-LSTM Model for Sentence Summarization.
- Author
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Wenfeng Liu, Peiyu Liu, Yuzhen Yang, Yaling Gao, and Jing Yi
- Subjects
MACHINE learning ,ARTIFICIAL intelligence ,COMPUTER programming ,INFORMATION retrieval ,ARTIFICIAL neural networks - Abstract
Generative Summarization is of great importance in understanding large-scale textual data. In this work, we propose an attention-based Tree-LSTM model for sentence summarization, which utilizes an attention-based syntactic structure as auxiliary information. Thereinto, block-alignment is used to align the input and output syntax blocks, while inter-alignment is used for alignment of words within that of block pairs. To some extent, block-alignment can prevent structural deviations on the long sentences and inter-alignment is capable of increasing the flexibility of the generation in the blocks. This model can be easily trained to end-to-end mode and deal with any length of the input sentences. Compared with several relatively strong baselines, our model has achieved the state-of-art on DUC-2004 shared task. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
13. 11 most in-demand gen AI jobs companies are hiring for.
- Subjects
DEEP learning ,MACHINE learning ,ARTIFICIAL intelligence ,NATURAL language processing ,ARTIFICIAL neural networks ,TEXT summarization - Abstract
AI researcher AI is new territory for businesses, and there's still a lot to discover about the technology, which is why they're looking to hire AI researchers to help identify the best possible applications of AI within the business. AI chatbot developer Chatbots are one of the earliest and most common uses of gen AI in a business setting - it's very likely that you have interacted with an AI chatbot at some point in the past several years. AI researchers help develop new models and algorithms that will improve the efficiency of generative AI tools and systems, improve current AI tools, and identify opportunities for how AI can be used to improve processes or achieve business needs. [Extracted from the article]
- Published
- 2023
14. SEL: A unified algorithm for salient entity linking
- Author
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Trani, Salvatore, Lucchese, Claudio, Perego, Raffaele, Losada, David E., Ceccarelli, Diego, and Orlando, Salvatore
- Subjects
Computational Mathematics ,machine learning ,Settore INF/01 - Informatica ,Artificial Intelligence ,entity linking ,salient entities ,text summarization - Published
- 2018
15. Identifying High Quality Document–Summary Pairs through Text Matching
- Author
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Yang Xiang, Fangze Zhu, Qingcai Chen, Buzhou Tang, Xiaolong Wang, and Yongshuai Hou
- Subjects
text summarization ,deep learning ,long short-term memory ,noise reduction ,Receiver operating characteristic ,Microblogging ,business.industry ,Computer science ,Noise reduction ,Deep learning ,02 engineering and technology ,Machine learning ,computer.software_genre ,Automatic summarization ,020204 information systems ,Test set ,0202 electrical engineering, electronic engineering, information engineering ,Leverage (statistics) ,020201 artificial intelligence & image processing ,Social media ,Artificial intelligence ,business ,computer ,Document summary ,Information Systems - Abstract
Text summarization namely, automatically generating a short summary of a given document, is a difficult task in natural language processing. Nowadays, deep learning as a new technique has gradually been deployed for text summarization, but there is still a lack of large-scale high quality datasets for this technique. In this paper, we proposed a novel deep learning method to identify high quality document–summary pairs for building a large-scale pairs dataset. Concretely, a long short-term memory (LSTM)-based model was designed to measure the quality of document–summary pairs. In order to leverage information across all parts of each document, we further proposed an improved LSTM-based model by removing the forget gate in the LSTM unit. Experiments conducted on the training set and the test set built upon Sina Weibo (a Chinese microblog website similar to Twitter) showed that the LSTM-based models significantly outperformed baseline models with regard to the area under receiver operating characteristic curve (AUC) value.
- Published
- 2017
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