Summary of Mevaker: Conclusion Extraction and Allocation Resources For the Hebrew Language, by Vitaly Shalumov et al.
Mevaker: Conclusion Extraction and Allocation Resources for the Hebrew Language
by Vitaly Shalumov, Harel Haskey, Yuval Solaz
First submitted to arxiv on: 12 Mar 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 This paper introduces new datasets and models for summarization and conclusion extraction in Hebrew. The MevakerSumm dataset contains summaries based on State Comptroller and Ombudsman of Israel reports, while MevakerConc contains conclusions from the same reports. Two auxiliary datasets are also provided. To accompany these datasets, the authors introduce two models: HeConE for conclusion extraction and HeCross for conclusion allocation. All code, datasets, and model checkpoints are publicly available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates new tools to help summarize and understand reports in Hebrew. It gives us special datasets and models that can be used to extract important information from these reports. The models, like HeConE and HeCross, can help us quickly find the main points of a report. All the code and data is available for anyone to use. |
Keywords
» Artificial intelligence » Summarization
