Summary of Chapter 7 Review Of Data-driven Generative Ai Models For Knowledge Extraction From Scientific Literature in Healthcare, by Leon Kopitar et al.
Chapter 7 Review of Data-Driven Generative AI Models for Knowledge Extraction from Scientific Literature in Healthcare
by Leon Kopitar, Primoz Kocbek, Lucija Gosak, Gregor Stiglic
First submitted to arxiv on: 18 Nov 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 The paper reviews and compares abstractive NLP-based text summarization approaches with existing extractive summarization techniques. The development of summarization is traced from the 1950s to the introduction of pre-trained language models like BERT and GPT. A total of 60 studies were identified, with 24 evaluated for eligibility and 7 used for further analysis. Examples are provided, including a comparison between GPT-3 and state-of-the-art GPT-4 solutions in scientific text summarization. The paper concludes that natural language processing has not yet reached its full potential in generating brief textual summaries, highlighting concerns that need to be addressed before practical implementation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how computers can summarize text. It compares different ways of doing this, from old methods to new ones using special AI models like BERT and GPT. They found 60 studies on this topic, but only used 7 in their analysis. The authors show examples of how well these new models do, including comparing GPT-3 with a more advanced version called GPT-4. Overall, the paper says that computers are not yet very good at summarizing text and there are some issues that need to be fixed before they can be used. |
Keywords
» Artificial intelligence » Bert » Gpt » Natural language processing » Nlp » Summarization
