Summary of Key-element-informed Sllm Tuning For Document Summarization, by Sangwon Ryu et al.
Key-Element-Informed sLLM Tuning for Document Summarization
by Sangwon Ryu, Heejin Do, Yunsu Kim, Gary Geunbae Lee, Jungseul Ok
First submitted to arxiv on: 7 Jun 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 The proposed Key-Element-Informed Instruction Tuning for Summarization (KEITSum) method aims to improve the summarization capabilities of smaller-scale Large Language Models (sLLMs). By identifying key elements in input documents, KEITSum instructs sLLMs to generate summaries that capture these essential details. This approach has been tested on dialogue and news datasets, demonstrating improved relevance and reduced hallucinations compared to proprietary LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary KEITSum is a new way to help smaller language models create better summaries of text. It works by finding the most important parts of what’s being summarized and telling the model to focus on those points. This helps keep the summary relevant and accurate, even when dealing with longer documents. The results show that KEITSum can produce high-quality summaries that are comparable to those made by more powerful language models. |
Keywords
» Artificial intelligence » Instruction tuning » Summarization
