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] An optimized hybrid deep learning model based on word embeddings and statistical features for extractive summarization
Extractive summarization has recently gained significant attention as a classification
problem at the sentence level. Most current summarization methods rely on only one way of …
problem at the sentence level. Most current summarization methods rely on only one way of …
Mining eye-tracking data for text summarization
M Taieb-Maimon, A Romanovski-Chernik… - … Journal of Human …, 2024 - Taylor & Francis
In this study, we introduce and evaluate a novel extractive text summarization methodology,“
SummarEyes,” based on the visual interaction of the user with the text, using eye-tracking …
SummarEyes,” based on the visual interaction of the user with the text, using eye-tracking …
Deep differential amplifier for extractive summarization
For sentence-level extractive summarization, there is a disproportionate ratio of selected and
unselected sentences, leading to flatting the summary features when maximizing the …
unselected sentences, leading to flatting the summary features when maximizing the …
Flexible non-autoregressive extractive summarization with threshold: How to extract a non-fixed number of summary sentences
Sentence-level extractive summarization is a fundamental yet challenging task, and recent
powerful approaches prefer to pick sentences sorted by the predicted probabilities until the …
powerful approaches prefer to pick sentences sorted by the predicted probabilities until the …
Towards Proactively Forecasting Sentence-Specific Information Popularity within Online News Documents
Multiple studies have focused on predicting the prospective popularity of an online
document as a whole, without paying attention to the contributions of its individual parts. We …
document as a whole, without paying attention to the contributions of its individual parts. We …
TSTR: Too short to represent, summarize with details! intro-guided extended summary generation
S Sotudeh, N Goharian - arXiv preprint arXiv:2206.00847, 2022 - arxiv.org
Many scientific papers such as those in arXiv and PubMed data collections have abstracts
with varying lengths of 50-1000 words and average length of approximately 200 words …
with varying lengths of 50-1000 words and average length of approximately 200 words …
[PDF][PDF] Proactively Forecasting Sentence-Specific Information Popularity
SG Roy - 2022 - cdn.iiit.ac.in
Popularity prediction is a well-studied machine learning task with wide-ranging business
applications. The primary goal in a popularity prediction problem is to estimate the future …
applications. The primary goal in a popularity prediction problem is to estimate the future …
[PDF][PDF] Approaches to solving Single Document Extractive Summarisation
R Saravanan - 2022 - cdn.iiit.ac.in
Summarization involves reducing the size of the document without the loss of important
content. The reduced content is called the summary of a document. Summarization can be …
content. The reduced content is called the summary of a document. Summarization can be …