Summary of Intelligent Multi-document Summarisation For Extracting Insights on Racial Inequalities From Maternity Incident Investigation Reports, by Georgina Cosma et al.
Intelligent Multi-Document Summarisation for Extracting Insights on Racial Inequalities from Maternity Incident Investigation Reports
by Georgina Cosma, Mohit Kumar Singh, Patrick Waterson, Gyuchan Thomas Jun, Jonathan Back
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 framework, I-SIRch:CS, leverages natural language processing (NLP) and machine learning techniques to aggregate and analyze safety incident reports in healthcare, uncovering critical insights to prevent harm by identifying recurring patterns and contributing factors. The framework integrates concept annotation using the Safety Intelligence Research (SIRch) taxonomy with clustering, summarization, and analysis capabilities. By applying BART’s abstractive summarization model, informative and concise summaries are generated for each cluster, maintaining traceability via file and sentence IDs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a healthcare setting, thousands of safety incidents happen every year, but learning from these incidents is not effectively shared. This paper shows how artificial intelligence can help analyze incident reports to find important patterns and factors that contribute to harm. By using special computer programs to read and understand written data, the researchers created a system called I-SIRch:CS. This framework helps group similar sentences together while keeping track of where they came from. The results show that BART’s summarization model is good at making informative summaries. |
Keywords
» Artificial intelligence » Clustering » Machine learning » Natural language processing » Nlp » Summarization
