Summary of A Sentiment Consolidation Framework For Meta-review Generation, by Miao Li and Jey Han Lau and Eduard Hovy
A Sentiment Consolidation Framework for Meta-Review Generation
by Miao Li, Jey Han Lau, Eduard Hovy
First submitted to arxiv on: 28 Feb 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 A novel approach in natural language generation leverages Large Language Models (LLMs) to generate summaries, but questions remain about their ability to consolidate information and handle opinionated content. This paper focuses on meta-review generation, a sentiment summarization technique for the scientific domain. By understanding how human reviewers consolidate sentiment, we propose prompting methods for LLMs to generate high-quality meta-reviews. Empirical validation shows that these prompts outperform simple instructions in generating better meta-reviews. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research uses artificial intelligence to help summarize long documents and find the most important information. Right now, computers can write summaries of multiple documents, but it’s not clear if they really understand what the texts are saying. The goal is to make these computer-generated summaries more accurate by understanding how humans create summary reviews for scientific papers. To do this, the researchers looked at how people write these reviews and then developed new ways for computers to generate better summaries. |
Keywords
» Artificial intelligence » Prompting » Summarization
