Summary of Mathematical Derivation Graphs: a Task For Summarizing Equation Dependencies in Stem Manuscripts, by Vishesh Prasad et al.
Mathematical Derivation Graphs: A Task for Summarizing Equation Dependencies in STEM Manuscripts
by Vishesh Prasad, Brian Kim, Nickvash Kani
First submitted to arxiv on: 26 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Recent advancements in natural language processing (NLP), particularly with the emergence of large language models (LLMs), have significantly enhanced textual analysis. However, applying NLP to analyze mathematical equations and their relationships within texts has yielded mixed results. This paper takes an initial step towards understanding dependency relationships between mathematical expressions in STEM articles by analyzing a dataset sourced from arXiv corpus, containing 107 hand-labeled manuscripts with derivation graphs summarizing mathematical content. The study exhaustively evaluates analytical and NLP-based models to identify and extract derivation relationships for each article, comparing results with ground truth. The comprehensive testing reveals that both analytical and NLP models (including LLMs) achieve similar F1 scores (~40-50%) in extracting derivation graphs from articles, indicating that recent advances in NLP have not significantly improved mathematical text comprehension compared to simpler analytic models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can use computers to understand math equations and how they relate to each other. Researchers took a big collection of math papers and labeled which equations are connected. They then tested different computer programs to see if they could find these connections. The results showed that current computer programs aren’t very good at understanding math, but there’s still room for improvement. This study helps us better understand how computers can work with math equations in the future. |
Keywords
* Artificial intelligence * Natural language processing * Nlp
