CRHASum: extractive text summarization with contextualized-representation hierarchical-attention summarization network

Diao, Yuanan; Lin, Hongfei; Yang, Liang; Fan, Xiaochao; Chu, Yonghe; Wu, Di; Zhang, Dongyu

doi:10.1007/s00521-019-04638-3

Cited by 32 publications

(8 citation statements)

References 23 publications

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“…Furthermore, LSTM makes it easier to modify the parameters of whether the GRU takes less time to train [5]. [153], [291], [155] are studies where the writers focused on the GRU-based method for summarization tasks. 4) Restricted Boltzmann Machine (RBM): A randomprobability-distributed neural network (RBM) is a neural network with random probability distributions.…”

Section: ) Deep Learning Algorithmmentioning

confidence: 99%

A Survey of Automatic Text Summarization: Progress, Process and Challenges

et al. 2021

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Section: ) Deep Learning Algorithmmentioning

confidence: 99%

A Survey of Automatic Text Summarization: Progress, Process and Challenges

et al. 2021

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“…The model has two levels of attention mechanism applied at word level and at sentence level. A hybrid neural extractive text summarization model known as Contextualized-Representation Hierarchical-Attention Summarization (CRHA-Sum) network is proposed in [34]. The model has ability to learn contextual semantic meaning and features relation for the purpose of text summarization.…”

Section: Multi Document Summarization (Mds)mentioning

confidence: 99%

Improved Text Summarization of News Articles Using GA-HC and PSO-HC

et al. 2021

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Automatic Text Summarization (ATS) is gaining attention because a large volume of data is being generated at an exponential rate. Due to easy internet availability globally, a large amount of data is being generated from social networking websites, news websites and blog websites. Manual summarization is time consuming, and it is difficult to read and summarize a large amount of content. Automatic text summarization is the solution to deal with this problem. This study proposed two automatic text summarization models which are Genetic Algorithm with Hierarchical Clustering (GA-HC) and Particle Swarm Optimization with Hierarchical Clustering (PSO-HC). The proposed models use a word embedding model with Hierarchal Clustering Algorithm to group sentences conveying almost same meaning. Modified GA and adaptive PSO based sentence ranking models are proposed for text summary in news text documents. Simulations are conducted and compared with other understudied algorithms to evaluate the performance of proposed methodology. Simulations results validate the superior performance of the proposed methodology.

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“…Other situations require a textual summary of the content taught during the video conference as well as the main questions asked by the listeners. In this sense, Diao et al [5] perform text summarization with a neural network model using the attention mechanism to capture context information and relationships between sentences improving the performance of phrase regression for text summarization.…”

Section: Future Visionmentioning

confidence: 99%

“…music, life-style and gaming) and enabled social interaction. 5 This new video-conferencing-based age, however, still needs improvement. After some months in home-office or remote classes during the pandemic, there are a lot of facets regarding communication, ethical and user-experience issues that can be already pointed out.…”

mentioning

confidence: 99%

Shaping the Video Conferences of Tomorrow With AI

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Freitas

et al. 2020

Anais Estendidos Do XXVI Simpósio Brasileiro De Sistemas Multimídia E Web (WebMedia 2020)

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Before the COVID-19 pandemic, video was already one of the main media used on the internet. During the pandemic, video conferencing services became even more important, coming to be one of the main instruments to enable most social and professional human activities. Given the social distancing policies, people are spending more time using these online services for working, learning, and also for leisure activities. Videoconferencing software became the standard communication for home-office and remote learning. Nevertheless, there are still a lot of issues to be addressed on these platforms, and many different aspects to be reexamined or investigated, such as ethical and user-experience issues, just to name a few. We argue that many of the current state-of-the-art techniques of Artificial Intelligence (AI) may help on enhancing video collabo- ration services, particularly the methods based on Deep Learning such as face and sentiment analyses, and video classification. In this paper, we present a future vision about how AI techniques may contribute to this upcoming videoconferencing-age.

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CRHASum: extractive text summarization with contextualized-representation hierarchical-attention summarization network

Cited by 32 publications

References 23 publications

A Survey of Automatic Text Summarization: Progress, Process and Challenges

A Survey of Automatic Text Summarization: Progress, Process and Challenges

Improved Text Summarization of News Articles Using GA-HC and PSO-HC

Shaping the Video Conferences of Tomorrow With AI

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