Enhancements of attention-based bidirectional lstm for hybrid automatic text summarization
The automatic generation of a text summary is a task of generating a short summary for a
relatively long text document by capturing its key information. In the past, supervised …
relatively long text document by capturing its key information. In the past, supervised …
Se4exsum: An integrated semantic-aware neural approach with graph convolutional network for extractive text summarization
T Vo - Transactions on Asian and Low-Resource Language …, 2021 - dl.acm.org
Recently, advanced techniques in deep learning such as recurrent neural network (GRU,
LSTM and Bi-LSTM) and auto-encoding (attention-based transformer and BERT) have …
LSTM and Bi-LSTM) and auto-encoding (attention-based transformer and BERT) have …
CRHASum: extractive text summarization with contextualized-representation hierarchical-attention summarization network
The requirements for automatic document summarization that can be applied to practical
applications are increasing rapidly. As a general sentence regression architecture …
applications are increasing rapidly. As a general sentence regression architecture …
A reinforced topic-aware convolutional sequence-to-sequence model for abstractive text summarization
In this paper, we propose a deep learning approach to tackle the automatic summarization
tasks by incorporating topic information into the convolutional sequence-to-sequence …
tasks by incorporating topic information into the convolutional sequence-to-sequence …
[PDF][PDF] Abstractive text summarization using attentive sequence-to-sequence rnns
E Jobson, A Gutiérrez - 2016 - academia.edu
In this work, we aimed to emulate the baseline of state-of-the-art abstractive text
summarization models, with the intention of exploring different attention mechanisms upon …
summarization models, with the intention of exploring different attention mechanisms upon …
A survey of text summarization approaches based on deep learning
SL Hou, XK Huang, CQ Fei, SH Zhang, YY Li… - Journal of Computer …, 2021 - Springer
Automatic text summarization (ATS) has achieved impressive performance thanks to recent
advances in deep learning (DL) and the availability of large-scale corpora. The key points in …
advances in deep learning (DL) and the availability of large-scale corpora. The key points in …
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 …
A supervised method for extractive single document summarization based on sentence embeddings and neural networks
Extractive summarization consists of generating a summary by ranking sentences from the
original texts according to their importance and salience. Text representation is a …
original texts according to their importance and salience. Text representation is a …
Joint knowledge-powered topic level attention for a convolutional text summarization model
Abstractive text summarization (ATS) often fails to capture salient information and preserve
the original meaning of the content in the generated summaries due to a lack of background …
the original meaning of the content in the generated summaries due to a lack of background …
Plausibility-promoting generative adversarial network for abstractive text summarization with multi-task constraint
Abstractive text summarization is an essential task in natural language processing, which
aims to generate concise and condensed summaries retaining the salient information of the …
aims to generate concise and condensed summaries retaining the salient information of the …