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 …
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 …
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 …
TxLASM: A novel language agnostic summarization model for text documents
Abstract In Natural Language Processing (NLP) domain, the majority of automatic text
summarization approaches depend on a prior knowledge of the language and/or the …
summarization approaches depend on a prior knowledge of the language and/or the …
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 …
Advancing automatic text summarization: Unleashing enhanced binary multi-objective grey wolf optimization with mutation
MA Sheikh, M Bashir, MK Sudddle - Plos one, 2024 - journals.plos.org
Automatic Text Summarization (ATS) is gaining popularity as there is a growing demand for
a system capable of processing extensive textual content and delivering a concise, yet …
a system capable of processing extensive textual content and delivering a concise, yet …
Knowledge relation rank enhanced heterogeneous learning interaction modeling for neural graph forgetting knowledge tracing
L Li, Z Wang - Plos one, 2023 - journals.plos.org
Knowledge tracing models have gained prominence in educational data mining, with
applications like the Self-Attention Knowledge Tracing model, which captures the exercise …
applications like the Self-Attention Knowledge Tracing model, which captures the exercise …
Multi-information interaction graph neural network for joint entity and relation extraction
Y Zhang, Y Zhang, Z Wang, H Peng, Y Yang… - Expert Systems with …, 2024 - Elsevier
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 …
[HTML][HTML] Synset2Node: A new synset embedding based upon graph embeddings
F Jafarinejad - Intelligent Systems with Applications, 2023 - Elsevier
Due to the advances made in recent years, embedding methods caused a significant
increase in the accuracy of text or graph processing methods. Embedding methods exhibit a …
increase in the accuracy of text or graph processing methods. Embedding methods exhibit a …