An extract-then-abstract based method to generate disaster-news headlines using a DNN extractor followed by a transformer abstractor

Banerjee, Sumanta; Mukherjee, Shyamapada; Bandyopadhyay, Sivaji

doi:10.1016/j.ipm.2023.103291

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…An extract-then-abstract based method to generate disaster-news headlines using a DNN extractor followed by a transformer abstractor [244]. Extensive evaluation of transformer-based architectures for adverse drug events extraction [291].…”

Section: Fig 5 Ai Components For Nlpmentioning

confidence: 99%

“…Paraphrasing techniques are a linchpin in recognizing or generating expressions and sentences with akin meanings. With a keen focus on generating coherent and semantically akin paraphrases, this model, powered by a finelytuned Text-To-Text Transfer Transformer (T5) [244], [248], [250], [260], [165], [223], [290], [291], [305] XLNet Personality is a complex mix of traits that define how we think, act, and feel, and it's studied in various fields like healthcare and social media. smart computer models like XLNet to predict personality traits from text, making it more accurate and helpful.…”

Section: Fig 5 Ai Components For Nlpmentioning

confidence: 99%

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Evolving Conversations: A Review of Chatbots and Implications in Natural Language Processing for Cultural Heritage Ecosystems

Suryanto,

Wibawa,

Hariyono

et al. 2023

IJRCS

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Chatbot technology, a rapidly growing field, uses Natural Language Processing (NLP) methodologies to create conversational AI bots. Contextual understanding is essential for chatbots to provide meaningful interactions. Still, to date chatbots often struggle to accurately interpret user input due to the complexity of natural language and diverse fields, hence the need for a Systematic Literature Review (SLR) to investigate the motivation behind the creation of chatbots, their development procedures and methods, notable achievements, challenges and emerging trends. Through the application of the PRISMA method, this paper contributes to revealing the rapid and dynamic progress in chatbot technology with NLP learning models, enabling sophisticated and human-like interactions on the trends observed in chatbots over the past decade. The results, from various fields such as healthcare, organization and business, virtual personalities, to education, do not rule out the possibility of being developed in other fields such as chatbots for cultural preservation while suggesting the need for supervision in the aspects of language comprehension bias and ethics of chatbot users. In the end, the insights gained from SLR have the potential to contribute significantly to the advancement of chatbots on NLP as a comprehensive field.

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Section: Fig 5 Ai Components For Nlpmentioning

confidence: 99%

Section: Fig 5 Ai Components For Nlpmentioning

confidence: 99%

Evolving Conversations: A Review of Chatbots and Implications in Natural Language Processing for Cultural Heritage Ecosystems

Suryanto,

Wibawa,

Hariyono

et al. 2023

IJRCS

View full text Add to dashboard Cite

show abstract

“…Works with related goals can be divided into two major categories. The first category represents ontology-based approaches [25][26][27], while the second category concerns AIbased approaches [12,[28][29][30].…”

Section: Related Workmentioning

confidence: 99%

Towards Automated Situational Awareness Reporting for Disaster Management—A Case Study

2023

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Disasters do not follow a predictable timetable. Rapid situational awareness is essential for disaster management. People witnessing a disaster in the same area and beyond often use social media to report, inform, summarize, update, or warn each other. These warnings and recommendations are faster than traditional news and mainstream media. However, due to the massive amount of raw and unfiltered information, the data cannot be managed by humans in time. Automated situational awareness reporting could significantly and sustainably improve disaster management and save lives by quickly filtering, detecting, and summarizing important information. In this work, we aim to provide a novel approach towards automated situational awareness reporting using microblogging data through event detection and summarization. Therefore, we combine an event detection algorithm with different summarization libraries. We test the proposed approach against data from the Russo-Ukrainian war to evaluate its real-time capabilities and determine how many of the events that occurred could be highlighted. The results reveal that the proposed approach can outline significant events. Further research can be carried out to improve short-text summarization and filtering.

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“…So, the medical imaging system is chiefly focused by medical researchers for analyzing the disease affection range with more possible ranges [ 13 ]. The main two techniques here are taking X-ray and Tomography [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Deep Neural Approach for Segmenting the COVID Affection Area from the Lungs X-Ray Images

Vijayanandh

Shenbagavalli²

2023

New Gener. Comput.

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Nowadays, COVID severity prediction has attracted widely in medical research because of the disease severity. Hence, the image processing application is also utilized to analyze COVID severity identification using lungs X-ray images. Thus, several intelligent schemes were employed to detect the COVID-affected part of the lungs X-ray images. However, the traditional neural approaches reported less severity classification accuracy due to the image complexity score. So, the present study has presented a novel chimp-based Adaboost Severity Analysis (CbASA) implemented in the MATLAB environment. Hence, the lung's X-ray images are utilized to test the working performance of the designed model. All public imaging data sources contain more noisy features, so the noise features are removed in the initial hidden layer of the novel CbASA then the noise-free data is imported into the classification phase. Feature extraction, segmentation, and severity specification have been performed in the classification layer. Finally, the performance of the classification score has been measured and compared with other models. Subsequently, the presented novel CbASA has earned the finest classification outcome.

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An extract-then-abstract based method to generate disaster-news headlines using a DNN extractor followed by a transformer abstractor

Cited by 10 publications

References 12 publications

Evolving Conversations: A Review of Chatbots and Implications in Natural Language Processing for Cultural Heritage Ecosystems

Evolving Conversations: A Review of Chatbots and Implications in Natural Language Processing for Cultural Heritage Ecosystems

Towards Automated Situational Awareness Reporting for Disaster Management—A Case Study

A Hybrid Deep Neural Approach for Segmenting the COVID Affection Area from the Lungs X-Ray Images

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