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Posted: 09 Feb 2022 04:00

“Abstractive Summarization” February 2022 — summary from Crossref

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“Abstractive Summarization” February 2022 — summary from Crossref main image

The content below is machine-generated by Brevi Technologies’ NLG model, and the source content was collected from open-source databases/integrate APIs.


Crossref - summary generated by Brevi Assistant


Attentional sequence-to-sequence models based upon RNN have achieved appealing efficiencies in the automated abstractive summarization of modern technology. The speculative results on the LCSTS dataset show that the offered model can properly enhance the ROUGE ratings and can much better summarize the resource document while maintaining the vital details.

A massive amount of information is developed online every passing second. The International Data Corporation jobs that the overall amount of digital data distributed each year worldwide would sprout from 4. 4 zettabytes in 2013 to 180 zettabytes in 2025. The combination of vision and natural language methods has come to be an essential subject in both computer system vision and all-natural language processing research communities. Experiments reveal that the model is better than the standard models and performs far better than text summarization techniques that ignore visual technique.

In the previous few years, neural abstractive text summarization with sequence-to-sequence models have gained a lot of appeal. As a component of this study, we also created an open resource collection, specifically, Neural Abstractive Text Summarizer toolkit, for abstractive text summarization. We introduce a straightforward but versatile mechanism to learn an intermediate plan to ground the generation of abstractive summaries. Transformer-based sequence-to-sequence models are after that trained to generate the entity chain and afterwards proceed generating the summary conditions on the entity chain and the input.


This can serve as an example of how to use Brevi Assistant and integrated APIs to analyze text content.


Source texts:

  • https://doi.org/10.1088/1742-6596/1848/1/012057 - An Automatic Abstractive Text Summarization Model based on Hybrid Attention Mechanism.
  • https://doi.org/10.48175/ijarsct-2470 - An Overview of Natural Language Processing Models for Abstractive Text Summarization.
  • https://doi.org/10.1088/1742-6596/1856/1/012070 - MBA: A Multimodal Bilinear Attention Model with Residual Connection for Abstractive Multimodal Summarization.
  • https://doi.org/10.1145/3419106 - Neural Abstractive Text Summarization with Sequence-to-Sequence Models.
  • https://doi.org/10.1162/tacl_a_00438 - Planning with Learned Entity Prompts for Abstractive Summarization.

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