Summary of Salient Information Prompting to Steer Content in Prompt-based Abstractive Summarization, by Lei Xu et al.
Salient Information Prompting to Steer Content in Prompt-based Abstractive Summarization
by Lei Xu, Mohammed Asad Karim, Saket Dingliwal, Aparna Elangovan
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 explores the use of salient information extracted from source documents to enhance large language model (LLM) summarization prompts. The authors show that adding keyphrases to prompts can improve ROUGE F1 and recall, making generated summaries more similar to references and more complete. The number of keyphrases controls the precision-recall trade-off. Additionally, incorporating phrase-level salient information is superior to word- or sentence-level approaches. However, the impact on hallucination varies across LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make large language models better at summarizing texts by using important words and phrases from the original text. The researchers found that adding these keyphrases to prompts makes the generated summaries more accurate and complete. They also discovered that using phrases is better than using individual words or sentences. However, this technique doesn’t always improve things for all language models. |
Keywords
» Artificial intelligence » Hallucination » Large language model » Precision » Recall » Rouge » Summarization
