Summary of Long Text Outline Generation: Chinese Text Outline Based on Unsupervised Framework and Large Language Mode, by Yan Yan and Yuanchi Ma
Long text outline generation: Chinese text outline based on unsupervised framework and large language mode
by Yan Yan, Yuanchi Ma
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 method for Chinese outline generation combines an unsupervised framework with large models to address the challenge of segmenting plot boundaries in very long texts. The approach first generates chapter feature graph data based on entity and syntactic dependency relationships, which is then processed by a representation module using graph attention layers to learn deep embeddings. A Markov chain-based operator is designed to segment plot boundaries, and finally, a large model is employed to generate summaries of each plot segment and produce the overall outline. The performance of the proposed method is evaluated based on segmentation accuracy and outline readability, outperforming several deep learning models and large models in comparative evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to create outlines for very long texts, like fiction books. Currently, AI models struggle to do this well because they get confused by the many details and relationships within these texts. To solve this problem, the authors developed a special method that combines several techniques together. First, it creates a graph of chapter features based on the relationships between characters and sentences. Then, it uses attention layers to understand the importance of each chapter feature. Finally, it uses a Markov chain to identify the boundaries between different parts of the story, and then generates summaries for each part. The authors tested their method and found that it outperformed other AI models in creating readable outlines. |
Keywords
» Artificial intelligence » Attention » Deep learning » Unsupervised
