Methods › Natural Language Processing › Textual Meaning › SIRM
Skim and Intensive Reading Model
SIRM
Introduced by Guoxiu He et al. in Read Beyond the Lines: Understanding the Implied Textual Meaning via a Skim and Intensive Reading Model
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Skim and Intensive Reading Model, or SIRM, is a deep neural network for figuring out implied textual meaning. It consists of two main components, namely the skim reading component and intensive reading component. N-gram features are quickly extracted from the skim reading component, which is a combination of several convolutional neural networks, as skim (entire) information. An intensive reading component enables a hierarchical investigation for both local (sentence) and global (paragraph) representation, which encapsulates the current embedding and the contextual information with a dense connection.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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DFREC: DeepFake Identity Recovery Based on Identity-aware Masked Autoencoder 10 Dec 2024 · 0 repositories · arXiv:2412.07260
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Read Beyond the Lines: Understanding the Implied Textual Meaning via a Skim and Intensive Reading Model 3 Jan 2020 · 0 repositories · arXiv:2001.00572
Tasks archive 2025-07-28
4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Face Swapping | 1 |
| Reading Comprehension | 1 |
| Sensitivity | 1 |
| Sentence | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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