Summary of Simplifying Scholarly Abstracts For Accessible Digital Libraries, by Haining Wang and Jason Clark
Simplifying Scholarly Abstracts for Accessible Digital Libraries
by Haining Wang, Jason Clark
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Digital Libraries (cs.DL)
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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 proposed project aims to develop language models that can simplify scholarly abstracts from diverse disciplines, making them more comprehensible to a broader audience. To achieve this goal, the researchers created a corpus of over 3,000 pairs of abstracts and significance statements, which they used to fine-tune four language models. The outputs were evaluated quantitatively for accessibility and semantic coherence, as well as qualitatively for language quality, faithfulness, and completeness. The results show that the resulting models can improve readability by over three grade levels while maintaining fidelity to the original content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The goal of this project is to make scientific information more accessible to everyone. Right now, many people struggle to understand complex scientific texts because they are written in a way that only experts can understand. The researchers want to change this by creating computer models that can simplify these texts so that anyone can read and learn from them. |
