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Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach
Social media is used to categorise products or services, but analysing vast comments is time-
consuming. Researchers use sentiment analysis via natural language processing …
consuming. Researchers use sentiment analysis via natural language processing …
A systematic literature review of deep learning-based text summarization: Techniques, input representation, training strategies, mechanisms, datasets, evaluation, and …
Abstract Automatic Text Summarization (ATS) involves estimating the salience of information
and creating coherent summaries that include all relevant and important information from the …
and creating coherent summaries that include all relevant and important information from the …
[HTML][HTML] FuzzyTP-BERT: Enhancing extractive text summarization with fuzzy topic modeling and transformer networks
In the rapidly evolving field of natural language processing, the demand for efficient
automated text summarization systems that not only distill extensive documents but also …
automated text summarization systems that not only distill extensive documents but also …
Multi-task hierarchical heterogeneous fusion framework for multimodal summarization
With the rise of multimedia content on the internet, Multimodal Summarization has become a
challenging task to help individuals grasp vital information fast. However, previous methods …
challenging task to help individuals grasp vital information fast. However, previous methods …
Improving extractive summarization with semantic enhancement through topic-injection based BERT model
In the field of text summarization, extractive techniques aim to extract key sentences from a
document to form a summary. However, traditional methods are not sensitive enough to …
document to form a summary. However, traditional methods are not sensitive enough to …
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 …
extractive techniques, this survey presents a comprehensive overview, delving into state-of …
Enhanced sentence representation for extractive text summarization: Investigating the syntactic and semantic features and their contribution to sentence scoring
The primary challenge faced in extractive text summarization is related to the scoring of
sentences, with the critical factor for scoring being the manner in which the sentence …
sentences, with the critical factor for scoring being the manner in which the sentence …
Supervised weight learning-based PSO framework for single document extractive summarization
The need for automatic text summarization is natural: there is a huge volume of information
available online, which prompts for a widespread interest in extracting relevant information …
available online, which prompts for a widespread interest in extracting relevant information …
Multi-information interaction graph neural network for joint entity and relation extraction
Overlap situation where different triplets share entities or relations is a common challenge in
joint entity and relation extraction task. On the one hand, there is strong correlation between …
joint entity and relation extraction task. On the one hand, there is strong correlation between …