Summary of Enhance Long Text Understanding Via Distilled Gist Detector From Abstractive Summarization, by Yan Liu et al.
Enhance Long Text Understanding via Distilled Gist Detector from Abstractive Summarization
by Yan Liu, Yazheng Yang
First submitted to arxiv on: 10 Oct 2021
Categories
- Main: Computation and Language (cs.CL)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the challenge of understanding long texts in natural language processing. The authors recognize that lengthy articles often contain redundant words that are not crucial to their main ideas. To address this issue, they develop a method called Gist Detector, which distills knowledge from abstractive summarization models to identify key information. This supplementary component is then integrated into existing models to enhance long text understanding. Experimental results demonstrate significant performance improvements for document classification and other tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand big texts better! When we read a long article or essay, there’s usually lots of extra words that don’t really matter. The authors want to figure out how to find the important parts and ignore the rest. They create a special tool called Gist Detector that helps existing computer models focus on what’s crucial. This makes it easier for computers to understand long texts. The results show that this method works really well, making it a useful tool for many applications. |
Keywords
» Artificial intelligence » Classification » Natural language processing » Summarization
