Summary of Optimizing the Role Of Human Evaluation in Llm-based Spoken Document Summarization Systems, by Margaret Kroll and Kelsey Kraus
Optimizing the role of human evaluation in LLM-based spoken document summarization systems
by Margaret Kroll, Kelsey Kraus
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 evaluation paradigm for abstractive summarization of spoken documents leverages methodologies from the social sciences to address challenges in evaluating generative AI content. This shift is crucial due to LLMs’ creative abilities, fluency in producing speech, and capacity to abstract information from large corpora. The study provides detailed evaluation criteria and best practices guidelines for ensuring robustness, replicability, and trustworthiness in human evaluation studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research introduces a new way to evaluate summaries generated by powerful language models (LLMs). These models are great at creating summaries that sound like they were spoken, but it’s hard to tell if they’re actually good summaries. The researchers suggest using methods from social sciences to figure out what makes a good summary. They also give guidelines on how to design experiments and make sure the results are reliable. |
Keywords
» Artificial intelligence » Summarization
