Summary of Improving Long Text Understanding with Knowledge Distilled From Summarization Model, by Yan Liu et al.
Improving Long Text Understanding with Knowledge Distilled from Summarization Model
by Yan Liu, Yazheng Yang, Xiaokang Chen
First submitted to arxiv on: 8 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel approach to enhance long text understanding in natural language processing (NLP) is proposed, leveraging abstractive summarization techniques to extract the gist of lengthy documents. The Gist Detector model learns to identify the essential information in long texts and incorporates this insight into downstream models, improving their ability to comprehend lengthy documents. This method is evaluated on three tasks: long document classification, distantly supervised open-domain question answering, and non-parallel text style transfer. Experimental results demonstrate significant performance improvements for baseline models on all tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Our new approach helps computers better understand long pieces of writing by identifying the most important parts. It works by using a special kind of AI model that’s good at summarizing texts. This model helps other AI models understand what’s really important in a long piece of text, making it easier for them to make sense of it. |
Keywords
» Artificial intelligence » Classification » Natural language processing » Nlp » Question answering » Style transfer » Summarization » Supervised
