Summary of Cnnsum: Exploring Long-context Summarization with Large Language Models in Chinese Novels, by Lingxiao Wei et al.
CNNSum: Exploring Long-Context Summarization with Large Language Models in Chinese Novels
by Lingxiao Wei, He Yan, Xiangju Lu, Junmin Zhu, Jun Wang, Wei Zhang
First submitted to arxiv on: 3 Dec 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 CNNSum, a novel benchmark for long-context summarization based on Chinese novels. The dataset features human-driven annotations and consists of four subsets totaling 695 samples with varying lengths. The authors evaluate numerous Large Language Models (LLMs) and conduct case analyses to explore their strengths and limitations. The findings indicate that advanced LLMs like GPT-4o may still generate subjective commentary, while smaller models are more cost-effective for long-context summarization. The study also highlights the importance of prompt templates and version models in achieving better performance. Furthermore, the authors demonstrate that LLMs with RoPE-base scaled exhibit strong extrapolation potential. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to test how well language models can summarize really long texts. They make a special dataset called CNNSum that has 695 samples of Chinese novels with varying lengths. The researchers tested many different language models and found out what they’re good at and what they’re not so good at. One important thing they learned is that even the most advanced language models can still make mistakes by writing summaries that are too subjective. They also discovered that smaller language models are actually better for summarizing really long texts because they don’t get overwhelmed. The study also shows that the way you phrase your question affects how well the language model does, and that some models are better at guessing what to write than others. |
Keywords
» Artificial intelligence » Gpt » Language model » Prompt » Summarization
